NASA Technical Memorandum 104566, Vol. 28

SeaWiFS Technical Report Series

Stanford B. Hooker, Elaine R. Firestone, and James G. Acker, Editors

Volume 28, SeaWiFS Algorithms, Part 1

Charles R. McClain, Kevin Arrigo, Wayne E. Esaias, Michael Darzi,

Frederick S. Patt, Robert H. Evans, James W. Brown, Christopher W. Brown,

Robert A. Barnes, and Lakshmi Kumar

June 1995
NASA Technical Memorandum 104566, Vol. 28

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

James G. Acker, Technical Editor

Hughes STX, Lanham, Maryland

Volume 28, SeaWiFS Algorithms, Part 1

Charles R. McClain, Kevin Arrigo, and Wayne E. Esaias

Goddard Space Flight Center, Greenbelt, Maryland

Michael Darzi and Frederick S. Patt

General Sciences Corporation, Laurel, Maryland

Robert H. Evans and James W. Brown

University of Miami, Miami, Florida

Christopher W. Brown

National Research Council, Washington, DC

Robert A. Barnes

ManTech Environmental Technology, Inc., Wallops Island, Virginia

Lakshmi Kumar

Hughes STX, Lanham, Maryland

National Aeronautics and

Space Administration

Goddard Space Flight Center

Greenbelt, Maryland 20771

1995
SeaWiFS Algorithms, Part 1

Preface

The scope of the SeaWiFS Calibration and Validation Program encompasses a very broad variety of topics,

as evidenced in several previous volumes in the SeaWiFS Technical Report Series. Volume 28 further

demonstrates the breadth and complexity of the issues that the program must address, and provides further

justification for a comprehensive calibration and validation effort.

The chapters in this volume address operational algorithm issues, which include:

1) A description of the algorithms used in generating the level-2 masks and flags for each pixel, which

are subsequently applied during the level-3 binning to exclude poor quality data;

2) An algorithm for masking ice- and cloud-contaminated pixels (also addressed in Chapter 1);

3) An algorithm for detecting pixels contaminated by coccolithophores (also addressed in Chapter 1);

4) An algorithm for removing out-of-band radiance from the observed radiances at each SeaWiFS band,

due to the finite filter response functions across the visible and near-infrared spectrum; and

5) A description of the software routines for converting level-1a counts to level-1b calibrated radiances.

Greenbelt, Maryland — C. R. McClain

January 1995

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C. McClain, K. Arrigo, W. Esaias, M. Darzi, F. Patt, R. Evans, J. Brown, C. Brown, R. Barnes, L. Kumar

Table of Contents

Prologue .................................................................................................1

1. SeaWiFS Quality Control Masks and Flags: Initial Algorithms and Implementation Strategy ..........3

1.1 Introduction ......................................................................................... 3

1.2 Masks and Flags .....................................................................................4

2. Cloud and Ice Detection at High Latitudes for Processing of CZCS Imagery .......................... 8

2.1 Introduction ......................................................................................... 8

2.2 Methods .............................................................................................8

2.3 Results and Discussion ...............................................................................9

2.4 Conclusions ........................................................................................ 12

3. Classification of Coccolithophore Blooms in Ocean Color Imagery ................................... 13

3.1 Introduction ........................................................................................13

3.2 Methods ........................................................................................... 13

3.3 Classification Results ............................................................................... 14

3.4 Discussion ..........................................................................................19

4. A Proposed On-Orbit, Out-of-Band Correction Scheme for SeaWiFS ................................ 20

4.1 Specification ....................................................................................... 20

4.2 Out-of-Band Correction ............................................................................ 22

4.3 Implementation .................................................................................... 22

4.4 Concluding Remarks ............................................................................... 25

4.5 Data Access ........................................................................................ 25

5. Algorithm for the Application of the Sensor Calibration for SeaWiFS Level-2 Processing ............ 26

5.1 Introduction ........................................................................................26

5.2 The calibrate_l1a Algorithm ..................................................................... 26

5.3 The get_cal Algorithm ............................................................................ 29

5.4 Sensor Calibration Table ........................................................................... 31

Appendix A ............................................................................................ 33

Glossary ...............................................................................................34

Symbols ................................................................................................ 34

References ............................................................................................ 35

The SeaWiFS Technical Report Series ............................................................ 36

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C. McClain, K. Arrigo, W. Esaias, M. Darzi, F. Patt, R. Evans, J. Brown, C. Brown, R. Barnes, L. Kumar

Abstract

This document provides five brief reports that address several algorithm investigations sponsored by the Calibration and Validation Team (CVT) within the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) Project.

This volume, therefore, has been designated as the first in a series of algorithm volumes. Chapter 1 describes

the initial suite of masks, used to prevent further processing of contaminated radiometric data, and flags, which

are employed to mark data whose quality (due to a variety of factors) may be suspect. In addition to providing

the mask and flag algorithms, this chapter also describes the initial strategy for their implementation. Chapter 2

evaluates various strategies for the detection of clouds and ice in high latitude (polar and sub-polar regions)

using Coastal Zone Color Scanner (CZCS) data. Chapter 3 presents an algorithm designed for detecting and

masking coccolithophore blooms in the open ocean. Chapter 4 outlines a proposed scheme for correcting the

out-of-band response when SeaWiFS is in orbit. Chapter 5 gives a detailed description of the algorithm designed

to apply sensor calibration data during the processing of level-1b data.

Prologue

The purpose of the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) Project is to obtain valid ocean color data

of the world ocean for a five-year period, to process that

data in conjunction with ancillary data to meaningful biooptical fields [K(490), chlorophyll a, and Coastal Zone

Color Scanner (CZCS) pigment], and to make that data

readily available to the user community through the GSFC

DAAC. As part of this effort, the SeaWiFS Calibration and

Validation Team (CVT) has three main tasks:

1) Calibration of the SeaWiFS instrument;

2) Development and validation of the operational

atmospheric correction algorithm; and

3) Development and validation of the derived biooptical algorithms and products.

Some of this work will be done internally at GSFC, while

the remainder will be done externally at other institutions.

NASA and the Project place the highest priority on assuring the accuracy of derived water-leaving radiances globally, and over the entire mission. These activities are discussed in detail in The SeaWiFS Calibration and Validation Plan (McClain et al. 1992).

Because many of the studies and development efforts

under the auspices of the Calibration and Validation Program are not extensive enough to require dedicated volumes of the SeaWiFS Technical Report Series, the CVT

has decided to publish volumes composed of brief, but topically specific, chapters. These volumes have been termed

Case Studies and include Volumes 13, 19, and 27. Volume

28 deals with the mask and flag algorithms, the sensor

calibration algorithm, and a sensor stray light correction

algorithm. This volume, therefore, has been designated as

the first in a series of Algorithm volumes.

The masks and flags are used in the level-2 processing

for quality control. Masks identify pixels unsuitable for

level-2 processing, e.g., land and clouds. Flags identify pixels where the bio-optical products may be of questionable

validity, e.g., shallow water and coccolithophore blooms.

The masks and flags are applied at the pixel level and are

included in the level-2 products. They are also used in

determining which pixels are included in the level-3 products. Most of the masks and flags are based on simple

criteria, which are described in an overview chapter. More

detailed discussions on the cloud and ice mask algorithm

and the coccolithophore detection flag required separate

chapters.

A short synopsis of each chapter in this volume is given

below.

1. SeaWiFS Quality Control Masks and Flags:

Initial Algorithms and Implementation Strategy

A variety of conditions related to geography, geometry,

sensor response, and atmospheric and oceanic optical properties can render a pixel either inappropriate for processing

to SeaWiFS level-2 geophysical values, or may cause the

level-2 values to be of questionable validity. Incorporated

into the level-2 processing scheme are 16 quality control

(QC) masks and flags. Masks prevent the completion of

data processing for affected pixels. Flags simply indicate

that a quality test has failed, but processing of the data

will continue. The results of these quality tests are stored

within the level-2 products and can be displayed with the

image data. The initial suite and the associated algorithms

for the masks and flags are described.

2. Cloud and Ice Detection at High

Latitudes for Processing CZCS Imagery

A cloud detection algorithm, which utilizes an albedo

threshold at 750 nm (α750), was tested on CZCS images of

the Southern Ocean. It was found that reducing α750 from

1.6% to 0.9% resulted in a marked improvement in cloud

detection, masking virtually all of the pixels with negative

water-leaving radiances in the ice pack and along the edges

of clouds. It was also found that this method was highly

1
SeaWiFS Algorithms, Part 1

sensitive to surface pressure, with a 2% change in pressure

resulting in a change of up to 50% in computed pigment

concentration, along with a large difference in the number

of masked pixels. This method appears to perform well

for both low and high reflectance waters, such as in the

presence of a large coccolithophore bloom. Manipulating

the value of α750 allowed masking of those pixels contaminated by sun glint. These results suggests that high latitude scenes previously rejected during the CZCS global

processing may be salvaged by properly applying masks.

3. Classification of Coccolithophore

Blooms in Ocean Color Imagery

Blooms of the coccolithophore Emiliania huxleyi, which

occur in the surface layer of the ocean, are characterized

by high ocean volume reflectance. This high reflectance biases the estimation of satellite-derived geophysical parameters, e.g., pigment concentration, that are based on waterleaving radiance measurements. To avoid erroneous estimates of geophysical parameters in the presence of blooms,

it is desirable to implement a simple classification algorithm to flag their occurrence. The development of a supervised, multispectral classification algorithm is described

in this chapter. The algorithm groups picture elements

(pixels) of the imagery into coccolithophore bloom and

non-bloom classes, based on the normalized water-leaving

radiances of each pixel.

4. A Proposed On-Orbit, Out-of-Band

Correction Scheme for SeaWiFS

Out-of-band responses for the eight SeaWiFS bands

are elements of the instrument’s radiometric calibration.

In that calibration, the instrument views a broad area

of known radiance, and the output from the bands are

recorded in counts. The counts from each band include the

out-of-band contribution, and these out-of-band contributions are functions of the spectral shape of the source that

is measured. The SeaWiFS laboratory calibration, therefore, has the out-of-band correction for a 2,850 K source

factored into its results. If the instrument measures a

source with that particular spectral shape, those measurements automatically contain appropriate out-of-band corrections. The prelaunch calibration equations for SeaWiFS

contain correction terms that convert the out-of-band responses from those for a 2,850 K source to those for a

5,900 K source. As a result, the SeaWiFS calibration equations now have the out-of-band correction for a 5,900 K

source factored into them. The 5,900 K spectral shape

closely duplicates the spectral shape for SeaWiFS ocean

measurements. The errors arising from the use of the

5,900 K out-of-band corrections for ocean measurements

are estimated to be small, on the order of a few tenths of

a percent. If an alternate out-of-band correction is to be

used, then the 5,900 K correction must be removed from

the measurement results and a new out-of-band correction

inserted in its place. An out-of-band correction based on

the actual measurements from SeaWiFS, plus the procedure for its implementation, is presented here.

5. Algorithm for the Application of the Sensor

Calibration for SeaWiFS Level-2 Processing

The processing of SeaWiFS level-1 data to level-2 requires that the sensor calibration be applied to the raw

counts, prior to the derivation of geophysical values. The

algorithm described herein is the implementation of the

sensor calibration approach of Barnes et al. (1994b). The

algorithm is designed for operational use by the SeaWiFS

Project, and works on individual scan lines of SeaWiFS

level-1a data, returning sensor-calibrated radiance values

(level-1b data). Calibration data used in the calculations

are stored in the sensor calibration table, which is also

described.

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C. McClain, K. Arrigo, W. Esaias, M. Darzi, F. Patt, R. Evans, J. Brown, C. Brown, R. Barnes, L. Kumar

Chapter 1

SeaWiFS Quality Control Masks and Flags:

Initial Algorithms and Implementation Strategy

Charles R. McClain

Goddard Space Flight Center

Greenbelt,Maryland

Robert H. Evans

James W. Brown

University of Miami

Miami,Florida

Michael Darzi

General Sciences Corporation

Laurel,Maryland

Abstract

A variety of conditions related to geography, geometry, sensor response, and atmospheric and oceanic optical

properties can render a pixel either inappropriate for processing to SeaWiFS level-2 geophysical values, or may

cause the level-2 values to be of questionable validity. Incorporated into the level-2 processing scheme are 16

QC masks and flags. Masks prevent the completion of data processing for affected pixels. Flags simply indicate

that a quality test has failed, but processing of the data will continue. The results of these quality tests are

stored within the level-2 products and can be displayed with the image data. The initial suite and the associated

algorithms for the masks and flags are described.

1.1 INTRODUCTION

The SeaWiFS Project has been tasked by the Science

Working Group (SWG) to provide highly accurate data

products, e.g., water-leaving radiances to within ±5% and

chlorophyll a to within ±35% (Hooker et al. 1992). To accommodate these goals, the SeaWiFS Project Calibration

and Validation element has undertaken the tasks of identifying conditions that might produce spurious results, and

defining the quantitative algorithms to test for each condition.

SeaWiFS will routinely collect global 4 km data. Much

of the data, therefore, will be contaminated by cloud or

land, i.e., the pixels will contain radiances reflected from

either clouds or land masses, and for that reason these

data will not be suitable for level-2 processing to SeaWiFS

standard products (Firestone and Hooker 1995). Other

conditions related to either the sensor performance, e.g.,

stray light, or optical properties in the atmosphere and

ocean, e.g., high aerosol optical thickness and coccolithophore blooms, respectively, can cause the derived products

to be of questionable validity. In many cases, algorithms

for detecting these conditions have been developed; but in

other cases, the algorithms require further development,

e.g., dust aerosols (Carder et al. 1991 and Toratani and

Fukushima 1993), and Trichodesmium blooms (Subramaniam and Carpenter 1994). In the latter case, these conditions are not explicitly included in the initial suite of

quality control parameters.

The results from the global CZCS data processing project (Feldman et al. 1989) demonstrated that much care

must be taken in data QC (McClain et al. 1992, McClain

et al. 1993, and Evans and Gordon 1994). Despite the

QC efforts undertaken during the CZCS global processing

and the large percentage of rejected scenes, some erroneous

data, e.g., cloud-contaminated data (Martin 1992), were

approved and included in the level-3 products. The CZCS

global processing QC protocols incorporated both automated techniques—primarily cloud, sensor ringing, and

sun glint masks—and interactive QC techniques, i.e., visual screening of level-2 and level-3 products. The interactive evaluations were quite time-consuming and rather

subjective, e.g., the anomalous water-leaving radiance category. The method also eliminated the entire scene if any

portion of the scene appeared questionable, rather than

rejecting data on a pixel-by-pixel basis. This approach

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SeaWiFS Algorithms, Part 1

Table 1. SeaWiFS level-2 masks and flags.

Parameter Mask/Flag Algorithm Name Required Quantities

Engineering Tolerance Flag 32 Red Limit ranges.

Tilt Flag

Solar Zenith Angle Flag SOLZEN1 Maximum value.

Spacecraft Zenith Angle Flag SATZEN1 Maximum value.

High Lt 1 Mask HIGHLT1 8 Knee radiances.

Stray Light Flag STRAYLIGHT1 Number of along-track pixels.

Number of along-scan pixels.

Missing Ancillary Data Flag ANCIL1

Land Mask LAND1 Global land mask file.

Bathymetry Flag COASTZ1 Global 0–30 m depth mask file.

Clouds and Ice Mask CLDICE1 Albedo.

Sun Glint Mask SUNGLINT1 Fraction of F0(865). 2

Atmospheric Correction Failure Mask EPSILON1 Acceptable range of  values.

High τa(865) 3 Flag HIGHTAU1 Maximum value.

Negative LWN (λ) 4 Flag NEGLW1

Low LWN (555) Flag LOWLW1 Minimum value.

Coccolithophore Flag COCCOLITH1 Minimum LWN (443).

Minimum LWN (555).

LWN (2:5) range. 5

LWN (4:5) range. 5

LWN (2:4) range. 5

Turbid Case-2 Water Flag TURBIDW1 Q factor value.

Chlorophyll Algorithm Failure Flag CHLOR1 Rrs(1:3) range. 6

Rrs(2:5) range. 6

1 Total radiance.

2 Extraterrestrial solar irradiance.

3 Aerosol radiance.

4 Normalized water-leaving radiance.

5 LWN (2:5) = LWN (443)/LWN (555): LWN (4:5) = LWN (510)/LWN (555): and LWN (2:4) = LWN (443)/LWN (510).

6 Rrs(1:3)= Rrs(412)/Rrs(443) and Rrs(2:5) = Rrs(443)/Rrs(555), where Rrs is the remote sensing reflectance.

eliminated quite a bit of good data in order to avoid the

inclusion of notably poor quality data in the level-3 fields.

The SeaWiFS QC methodology seeks to automate the

testing so that interactive evaluations will be minimized.

The masks and flags will be applied either on a line-by-line

or a pixel-by-pixel basis, depending on the QC parameter. Masks prevent the completion of data processing for

affected pixels, while flags simply indicate that a quality

test has failed, but data processing will continue.

The results of each of the pixel-by-pixel tests will be

preserved in the level-2 products as a 2-byte metadata

field. This field, called l2_flags, is associated with each

pixel, and each bit of l2_flags is assigned to a specific

mask or flag. The line-by-line flag and mask information

is saved in the level-2 file on a scan line-by-scan line basis,

rather than on a pixel-by-pixel basis and, therefore, is not

stored in the 16-bit quality field. These fields can be displayed separately. The mask and flag information is used

in the level-3 processing to determine the quality of the

binned data. How the flags and masks are applied in generating the level-3 products will be discussed in a separate

document.

The definition and refinement of the QC parameter

suite and the respective algorithms involved will be an

continuing activity within the SeaWiFS Project. At each

major reprocessing, the Project will have the opportunity

to add and delete QC parameters, as well as update the

algorithms.

1.2 MASKS AND FLAGS

Table 1 provides the list of initial mask and flag parameters, the algorithm names, and the required quantities to implement each criterion. The algorithm names are

stored as attributes of the l2_flags field. Initially, there

are two line-by-line flags, Engineering Tolerance and Tilt,

which are generated by the level-0 to level-1 conversion

program. In this processing step, there are no line-by-line

masks. In the case of masked pixels, the level-1 radiances

are assigned to the level-2 normalized water-leaving radiance and aerosol radiance products, and a value of zero

is assigned to the other products (i.e., pigment concentrations, diffuse attenuation, epsilon, and optical thickness

fields).

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C. McClain, K. Arrigo, W. Esaias, M. Darzi, F. Patt, R. Evans, J. Brown, C. Brown, R. Barnes, L. Kumar

1.2.1 Descriptions

Engineering Tolerance: A bit per scan line is reserved

within the level-2 file, for each of 32 sensor engineering

parameters, to indicate if the parameters are outside the

normal operating range. These bits are stored, for level-2

as well as level-1a products, in a 4 byte metadata array associated with each scan line, called eng_qual. Table 2 provides the sensor engineering parameter operating ranges

and red limits, i.e., values between which the instrument

should normally operate.

Tilt: The scan line ranges for each tilt state (−20◦,

+20◦, 0◦, and tilting) are stored as metadata tilt_ranges

and tilt_flags in the level-1a and level-2 products. In

addition, the tilt angle (tilt) is stored as metadata for

each scan line.

Solar Zenith Angle: All pixels having solar zenith angles greater than 70◦ will be flagged because of the uncertainty in atmospheric correction for large optical path

lengths. Note also that as the angle of incidence exceeds

70◦, the Fresnel reflectance increases rapidly, causing a corresponding limitation in the amount of radiation entering

the water column.

Spacecraft (S/C) Zenith Angle: All pixels with pixelto-spacecraft zenith angles greater than 45◦ will be flagged.

Local area coverage (LAC) will extend to scan angles of

58.3◦. The global area coverage (GAC) data recorded onboard will be truncated at scan angles of ±45◦, although

due to the Earth’s curvature and sensor tilting, the S/C

zenith angle will exceed 45◦ for GAC coverage along the

edges of the scan. At 45◦, the pixel area begins to increase

rapidly as the scan angle is increased. At 45◦, for example,

the pixel area is increased by factors of about 2.5 (0◦ tilt)

and 3.5 (20◦ tilt) over the pixel area at nadir, thus making

interpretation of the products less definite. Large scan angles also increase the difficulty of both accurate navigation

and atmospheric corrections. The CZCS data were limited

to scan angles of ±39.34◦, and the distortion at scan edges

was considerable. Given the improved performance and

navigation capabilities of SeaWiFS and SeaStar, respectively, it is felt that reasonable results should be attainable

out to ±45◦.

High Lt: All pixels having radiances less than the knee

value in one or more bands will be flagged. Radiances

above the knee in the bilinear gain are quantified at substantially lower precision than radiances below the knee

(Barnes et al. 1994b). The reduction in precision ranges

from 1:17 at 412 nm to 1:64 at 865 nm.

Stray Light: There are a number of sources of scattered

light within the SeaWiFS sensor which cause light from

adjacent pixels to contribute to the radiance of the pixel of

interest (Barnes et al. 1994a). The problem is serious only

in the vicinity of very bright pixels. The effect decreases

to an acceptable level at about 4 GAC pixels in the scan

direction and one GAC pixel along track, based on the

prelaunch characterization data provided by Hughes Santa

Barbara Research Center (SBRC). Due to the uncertainty

in the exact location of the edge of the bright object in the

GAC data, these are conservative values. The stray light

flag will be applied to pixels adjacent to any pixel masked

for high Lt.

Missing Ancillary Data: If a gap in the ancillary data

exists, interpolated values will be used. This flag identifies

where the gaps in the original data were located. Interpolation of ancillary data by the SeaWiFS Project should

only be necessary for Television and Infrared Observation

Satellite (TIROS) Operational Vertical Sounder (TOVS)

ozone data which will be used if Total Ozone Mapping

Spectrometer (TOMS) data are not available. The TOMS

and the surface meteorological data (pressure, winds, relative humidity) are to be provided to the Project as global

gridded fields.

Land: This criterion will apply to pixels completely or

partially over land, as determined using a global land mask

generated at the University of Miami.

Bathymetry: Pixels over water with depths shallower

than, or equal to, 30 m will be flagged. A bathymetry

flag file satisfying the depth criteria was generated using a

global bathymetry database with a 5 minute spatial resolution. If a different depth threshold is to be used, a different

global flag file must be generated and input to the level-2

processing program.

The purpose of this flag is to identify areas where bottom reflection may influence the water-leaving radiances,

resulting in erroneous pigment concentration and diffuse

attenuation estimates. Many coastal water masses possess

sufficient clarity that this effect may be present. Using (1)

(Gordon and Morel 1983),

K(440) = 0.0168 + 0.1031C0.707, (1)

where K(440) is the diffuse attenuation coefficient at

440 nm, a 30 m penetration depth corresponds to C, a pigment concentration (chlorophyll a + phaeopigment) of only

0.08 mg m−3, which should be sufficiently deep.

Clouds and Ice: Pixels having an albedo at 865 nm

greater than 0.9% will be flagged (Arrigo and McClain

1995). The albedo computation accounts for the seasonal

variations in solar zenith angle, and the use of 865 nm minimizes the influence of Rayleigh radiance and highly reflective oceanic waters which can occur with the presence of

coccolithophore blooms and riverine sediment plumes.

Glint: Pixels having a glint radiance of Lg(865) greater

than 0.005F0(865) will be masked. F0 is the extraterrestrial solar constant adjusted for the Earth-sun distance.

This formula is the expression used in the global CZCS

processing (Evans and Gordon 1994). The expression for

glint radiance (Viollier et al. 1980 and Gregg et al. 1993)

is:

Lg(λ) = ρT0(λ, θ0)F0(λ)PW (θ, φ, θ0, φ0, W)

4 cos θ cos4 θn

. (2)

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SeaWiFS Algorithms, Part 1

Table 2. Engineering telemetry parameters and scaling for SeaWiFS. The table includes absolute limits and Red Limits

for the operation of SeaWiFS.

Analog Telemetry Point Conversion Absolute Limits Red Limits

Slope Intercept Low High Low High

Band 1/2 FPA1 Temperature −0.2667 66.667 −1.334 66.667 5.0 45.0

Band 3/4 FPA Temperature −0.2667 66.667 −1.334 66.667 5.0 45.0

Band 5/6 FPA Temperature −0.2667 66.667 −1.334 66.667 5.0 45.0

Band 7/8 FPA Temperature −0.2667 66.667 −1.334 66.667 5.0 45.0

Telescope Motor Temperature −0.2667 66.667 −1.334 66.667 5.0 45.0

Tilt Base Temperature −0.2667 66.667 −1.334 66.667 5.0 45.0

Tilt Platform Temperature −0.2667 66.667 −1.334 66.667 5.0 45.0

Half-Angle Motor Temperature −0.2667 66.667 −1.334 66.667 5.0 45.0

Power Supply-A Input Current2 0.02 0.26 0.26 5.36 1.0 3.0

Power Supply-B Input Current2 0.02 0.26 0.26 5.36 1.0 3.0

+15 V Analog Power Voltage3 0.075 0.0 0.0 19.125 15.0 15.5

−15 V Analog Power Voltage3 −0.075 0.0 −19.125 0.0 −15.5 −15.0

+5 V Logic Power Voltage3 0.025 0.0 0.0 6.375 4.9 5.6

Power Supply Temperature −0.2667 66.667 −1.334 66.667 5.0 45.0

B1/B2 Post-Amplifier Temperature −0.2667 66.667 −1.334 66.667 5.0 45.0

Servo Driver Temperature −0.2667 66.667 −1.334 66.667 5.0 45.0

+30 V Servo Power Voltage3 0.15 0.0 0.0 38.25 28.5 31.0

+21 V Servo Power Voltage3 0.1044 0.0 0.0 26.622 20.0 22.0

−21 V Servo Power Voltage3 −0.1044 0.0 −26.622 0.0 −22.0 −20.0

+5 V Servo Power Voltage3 0.025 0.0 0.0 6.375 4.9 5.6

Angular Momentum Speed3,4 8.52 −377.0 −377.0 1795.6 1215.0 1255.0

Tilt Platform Position3 1.44 0.0 0.0 367.2 0.0 360.0

Tilt Base Position3 1.44 0.0 0.0 367.2 0.0 360.0

+28 V Heater Power3 0.14 0.0 0.0 35.7 27.0 29.0

Telescope-A Motor Current3,5 0.0024 0.0 0.0 0.612 0.1 0.4

Telescope-B Motor Current3,5 0.0024 0.0 0.0 0.612 0.1 0.4

Half-Angle-A Motor Current3,5 0.0024 0.0 0.0 0.612 0.1 0.4

Half-Angle-B Motor Current3,5 0.0024 0.0 0.0 0.612 0.1 0.4

Servo-A Phase Error3,5 0.01 −1.25 −1.25 1.25 −1.0 1.0

Servo-B Phase Error3,5 0.01 −1.25 −1.25 1.25 −1.0 1.0

Angular Momentum Compensation

A-Motor Current3,4,5 0.016 0.0 0.0 4.08 0.08 0.6

Angular Momentum Compensation

B-Motor Current3,4,5 0.016 0.0 0.0 4.08 0.08 0.6

1. Focal Plane Assembly

2. This parameter is dependent upon the choice of power supply. (It does not appear in the instrument discrete telemetry, so it is

assumed to be a spacecraft field.)

3. This parameter is dependent upon the analog power on.

4. This parameter is dependent upon the angular momentum compensation on.

5. This parameter is dependent upon the choice of servo-A or -B.

In this expression, ρ represents the Fresnel reflectivity, and

PW is the probability of seeing sun glint in the spacecraft

direction (θ, φ), when the wind speed is W and the sun

position is (θ0, φ0), as described by Cox and Munk (1954).

The total downward transmittance of irradiance is represented by T0, and θn is defined as

θn = arccos 

cos θ + cos θ0

2 cos ω



, (3)

where

cos 2ω = cos θ cos θ0 + sin θ sin θ0 cos(φ − φ0). (4)

Atmospheric Correction Algorithm Failure: Pixels will

be masked and assigned the level-1 total radiance values,

where the atmospheric correction algorithm (Gordon and

Wang 1994) fails to return  values within a specified range.

Strategies for using correction parameters from nearby pixels will be evaluated after launch.

High τa: Pixels having a τa(865) greater than a fixed

threshold will be flagged. This flag is due to the fact that

aerosol corrections become less reliable as atmospheric turbidity increases.

Negative LW : Any pixel having a computed LW < 0

will be flagged, and the normalized water-leaving value will

be set to 0. Previous experience with CZCS data show that

6
C. McClain, K. Arrigo, W. Esaias, M. Darzi, F. Patt, R. Evans, J. Brown, C. Brown, R. Barnes, L. Kumar

negative radiances tend to occur in cloud shadows (Arrigo

and McClain 1995), and can also occur when the waterleaving radiance is very small, such as in high pigment

concentration Case-1 and turbid Case-2 waters.

Coccolithophore: Using a modified version of the method of Brown and Yoder (1994a) and Brown (1995), pixels

selected as being contaminated by coccolithophores will be

flagged. The primary modification to the Brown and Yoder (1994a) algorithm will be to delete the aerosol radiance



La(670)

and Bands Saturated tests, which are not necessary with the pixel-by-pixel SeaWiFS processing. The

Bands Saturated test was intended to mask data contaminated by sun glint in the CZCS global processing data

products.

Low LW N (555): This flag denotes pixels where the normalized water-leaving radiance at 555 nm is below 0.21

mW cm−2 μm−1 sr−1, i.e., 75% of the clear water value

(Gordon and Clark 1981). The hinge point (Duntley et

al. 1974) in the water-leaving radiance spectrum is near

510 nm, so increases in pigment and particulate concentrations should increase the radiance above the clear water

value. Any pixel with a water-leaving radiance value substantially below the clear water value represents an anomalous condition.

Turbid Case-2: The irradiance reflectance algorithm,

recommended in Bricaud and Morel (1987) for discriminating between Case-1 and Case-2 waters, and updated

in Morel (1988), will be used to flag Case-2 water. The

condition for the limiting reflectance for Case-1 water at

555 nm is expressed as

Rlim(555) = 1 − 2.5B − (2.5B − 1)2 − 4.44B

2 , (5)

where

B = 0.33b

Kd

. (6)

The backscattering coefficient, bb, is defined as

bb(555) = 0.00075 + 

0.0063 − 0.00263 log(C)



C0.62, (7)

and Kd is the diffuse attenuation coefficient for downwelling irradiance (Gordon and Morel 1983),

Kd(555) = 0.0717 + 0.039C0.64. (8)

Because the operational bio-optical algorithm (Carder pers.

comm.) is based on Rrs, which is defined as the ratio of

water-leaving radiance to surface downwelling irradiance,

the turbid Case-2 flag will be set when Rrs exceeds a value

analogous to Rlim, i.e.,

Rrs(555) > (1 − ρ)Rlim(555)

Qn2 , (9)

where Q = 3.42 and the index of refraction n = 1.341.

Note that Rrs(λ) = LW N (λ)/F0(λ).

Chlorophyll Algorithm Failure: If the semi-analytic

model for chlorophyll a (Carder pers. comm.) fails to return a valid value, a reflectance ratio algorithm will be used

to compute chlorophyll a, and the pixel will be flagged.

Acknowledgments

The authors would like to thank Mike Jones, Eueng-nan Yeh,

and Bob Barnes for their comments.

7
SeaWiFS Algorithms, Part 1

Chapter 2

Cloud and Ice Detection at High

Latitudes for Processing CZCS Imagery

Kevin R. Arrigo

Charles R. McClain

Goddard Space Flight Center

Greenbelt,Maryland

Abstract

A cloud detection algorithm, which utilizes an albedo threshold at 750 nm (α750), was tested on CZCS images

of the Southern Ocean. It was found that reducing α750 from 1.6% to 0.9% resulted in a marked improvement

in cloud detection, masking virtually all of the pixels with negative water-leaving radiances in the ice pack and

along the edges of clouds. It was also found that this method was highly sensitive to surface pressure, with a

2% change in pressure resulting in a change of up to 50% in computed pigment concentration, along with a

large difference in the number of masked pixels. This method appears to perform well for both low and high

reflectance waters, such as in the presence of a large coccolithophore bloom. Manipulating the value of α750

allowed masking of those pixels contaminated by sun glint. These results suggest that high latitude scenes

previously rejected during the CZCS global processing may be salvaged by applying masks properly.

2.1 INTRODUCTION

CZCS, aboard the NIMBUS-7 satellite, was designed

to measure pigment concentrations in the upper ocean using the spectral characteristics of upwelled radiation. Different algorithms have been developed to derive in situ

pigment concentrations using ratios of measured radiance

at 440, 520, and 550 nm (Gordon et al. 1983 and Bricaud

and Morel 1987), but these algorithms assume cloud-free

skies. It is essential, therefore, that cloud contaminated

pixels be masked out before images are processed.

Seawater, and its dissolved and particulate constituents,

alter the spectral composition of visible (400–700 nm) radiation reaching the satellite sensor. This is not the case for

near-infrared (IR) radiation. Near-IR radiation is strongly

absorbed by water, resulting in a uniformly low albedo.

Clouds, in contrast, have a wide range of reflectivities at

the wavelengths measured by CZCS. Band 5, centered on

750 nm (± 50 nm) was the only CZCS band that did not

saturate over most clouds and land. Band 5 thus provides

the best contrast between clear water and clouds, and is

most commonly used in cloud masking algorithms.

Early cloud masking efforts utilized a simple constant

threshold method, whereby pixels above a fixed radiance

(or number of counts) were excluded as being cloud contaminated. For band 5, this threshold was usually assumed

to be 18 counts (Mueller pers. comm.) at low and intermediate latitudes, and 14 counts at higher latitudes. The

value of 21 counts was used in the CZCS global processing (Feldman et al. 1989). More sophisticated methods use

albedo at 750 nm (α750), calculated as the ratio of upwelled

to downwelled radiance, as an indication of the presence of

clouds (Eckstein and Simpson 1991). The albedo method

is superior to the fixed threshold method because it is able

to account for variations in the satellite viewing angle, solar zenith angle, and surface pressure. Empirical observations suggested that pixels with values for α750 >1.6%

were contaminated by clouds (Eckstein and Simpson 1991

and McClain et al. 1994). Other studies utilized even more

complex methods, such as textural analysis and edge detection, to identify the presence of clouds (Gallaudet and

Simpson 1991). Darzi (1992) provides an overview of cloud

masking algorithms for ocean-viewing sensors.

This paper presents the results of a study intended

to assess whether the threshold values reported in Eckstein and Simpson (1991), i.e., α750=1.6%, are applicable

to CZCS images of the Southern Ocean. The study also

sought to determine whether algorithms designed to identify cloud contaminated pixels could flag pixels influenced

by the presence of sea ice.

2.2 METHODS

More than 250 separate CZCS scenes of the Southern

Ocean were processed from level-1 to level-2 using a modified version of the SEAPAK program L2MULT (McClain

8
C. McClain, K. Arrigo, W. Esaias, M. Darzi, F. Patt, R. Evans, J. Brown, C. Brown, R. Barnes, L. Kumar

et al. 1991 and McClain and Yeh 1994a). The program

L2MULT requires input of the surface pressure, which is

then used to calculate the Rayleigh and oxygen optical

thicknesses. This modified version of L2MULT is capable of

reading surface pressure field files and deriving space- and

time-interpolated pressure values for each pixel within a

given scene. Ozone data from TOMS is also spatially interpolated to each pixel when TOMS data is available. Scenes

may thus be processed assuming that surface pressure and

columnar ozone are either spatially constant or variable.

L2MULT is also capable of deriving a cloud mask based on

either a count threshold for band 5, or the α750 threshold.

The user may select either method.

The scenes used in this analysis were predominantly of

the Ross Sea (November 1978–March 1979, 70◦ S to 78◦ S)

and the Weddell Sea (March 1986, 72◦ S to 78◦ S). Most

scenes included land, ice covered waters, open water regions, and cloud covered regions. Solar elevations at the

scene centers ranged from 23◦ to 50◦.

The following equation was used to calculate α750:

α750 = Lt(750)

t(750, θ)Li(750) × 100%, (10)

where Lt(750) is the total radiance at the sensor at 750 nm,

Li(750) is the incident light at 750 nm, t(750, θ) is the

diffuse transmittance between the surface and sensor at

750 nm, and θ is the satellite zenith angle.

The diffuse transmittance between the surface and sensor at 750 nm is calculated as

t(750, θ) = exp 

−(0.5τr + τoz + τox)

cos θ



, (11)

where τr, τoz, and τox are the Rayleigh (0.0255), ozone

(0), and oxygen (0.02) optical thicknesses, respectively, at

750 nm.

The incident light at 750 nm is computed as

Li(750) = t(750, θ0)F0, (12)

where F0 is the solar irradiance at 750 nm (mean = 125 mW

cm−2 μm−1 sr−1) and θ0 is the solar zenith angle. The diffuse transmittance between the sun and the ocean surface

at 750 nm, t(750, θ0), is calculated as

t

	

750, θ0


 = exp 

−(0.5τr + τoz + τox)

cos θ0



. (13)

For the sake of simplicity, much of the analysis presented below focused on a single CZCS scene [orbit number 37118, year 1986, day 60, 16:10:28 Greenwich Mean

Time (GMT)] which will be referred to below as scene 1;

however, the results were similar for all scenes tested. The

results of changes in the α750 threshold are also shown for a

scene with highly reflective waters containing a rich bloom

of coccolithophores (orbit number 8876, year 1980, day

209, 12:29:53 GMT) which will be referred to as scene 2.

Another test examined whether changes in the α750 threshold would mask pixels affected by sun glint in scene 3 (orbit

number 2573, year 1979, day 118, time 9:37:06).

2.3 RESULTS AND DISCUSSION

The following discussion of the results obtained in this

analysis addresses five distinct topics. The overall effect of

changing the α750 threshold value will be examined first.

A discussion of the changes that are induced by using variable surface pressure fields, rather than a uniform pressure

field, appears in Section 2.3.2. Variations in the constant

pressure value used in the analysis also has significant effects, which are discussed in Section 2.3.3. Section 2.3.4

examines the effect of changing the α750 threshold on images of high reflectance waters. Section 2.3.5 concerns the

use of cloud masking algorithms to flag areas that are affected by sun glint.

2.3.1 Changes in the Albedo Threshold

The frequency histogram of scene 1 (Fig. 1a), processed

using an α750 threshold of 1.6% (Eckstein and Simpson

1991) and a variable pressure field, revealed an unusual

pigment distribution. The unusual feature of this distribution was a pronounced spike at 44 mg m−3 (chlorophyll a

+ phaeopigments), i.e., a gray level equal to the maximum

value of 254. This spike consisted of 6,983 pixels, which

was 10% of the total number of pixels. Most of these pixels

were located along the edge of cloud patches and within

the sea ice pack (Plate 1†), suggesting that they were contaminated by the presence of clouds or ice, but were not

flagged during level-2 processing. Water-leaving radiances

for these pixels, at either 520 nm or 550 nm, were negative; consequently, the pigment value for these pixels was

set artificially high (equal to a gray level of 254, or a pigment concentration of 46.345 mg m−3). The level-2 processing algorithm in the SEAPAK software used this labeling algorithm as a secondary flagging mechanism. In

the case of the 520 nm band, this mechanism was utilized when the water-leaving radiance at 443 nm was less

than 0.15 mW cm−2 μm−1 sr−1. Negative water-leaving radiances result when unmasked pixels are contaminated by

thin clouds, or are unusually reflective in some other way,

i.e., because of sea ice or sun glint. As the calculation of

aerosol radiance depends on the assumption of a cloud-free

sky, the aerosol radiance is overestimated for these pixels.

The resulting calculation of water-leaving radiances at 520

or 550 nm will subsequently yield negative values due to

the unusually high aerosol radiances, as water-leaving radiance is calculated by subtracting the aerosol radiance

and the Rayleigh radiance from the total radiance.

† All color plates are located in the back of this document.

9
SeaWiFS Algorithms, Part 1

Fig. 1. Gray level frequency distributions for: a) the scene where α750 = 1.6% (Plate 1); b) the scene where

α750 = 1.0%; c) the scene where α750 = 0.7% (Plate 2); and d) the difference between the distributions shown

in a) and c).

10
C. McClain, K. Arrigo, W. Esaias, M. Darzi, F. Patt, R. Evans, J. Brown, C. Brown, R. Barnes, L. Kumar

The mean pigment concentration (plus or minus the

standard deviation) calculated for scene 1, after applying the cloud mask and removing all pixels with negative

water-leaving radiances, was 0.601 ± 2.596 mg m−3 (chlorophyll a + phaeopigments). The calculation was based on

59,562 valid pixels (Table 3). Reducing α750 to 1.0% lowered the computed pigment concentration to 0.456 ± 1.946

mg m−3 (chlorophyll a + phaeopigments), with 54,797 valid

pixels. Some pixels with negative water-leaving radiances

along the cloud and ice edges were still present, however,

although the number was reduced substantially, to 2,073

pixels (Fig. 1b). These pixels were not included in the

calculation of the mean.

Table 3. Spatially averaged pigment concentrations as a function of albedo threshold for Scene 1.

All pigment concentrations have units of mg m−3.

Pigment Concentration

Variable Constant† Valid

Albedo Pressure Pressure Pixels

1.6% 0.601 ± 2.596 0.671 ± 2.818 59,462

1.0% 0.456 ± 1.946 0.530 ± 2.254 54,797

0.7% 0.335 ± 1.134 0.406 ± 1.600 48,203

† 1,013.25 mb.

Further reducing α750, to 0.7%, resulted in the masking of virtually all the pixels with negative water-leaving

radiances in the ice pack and along the edges of clouds

(Plate 2). All except 132 of the pixels with pigment concentrations in excess of 44 mg m−3 (chlorophyll a + phaeopigments) were masked, leaving most of the previously unmasked pixels intact (Fig. 1c). This value of α750 results

in a more normal frequency distribution (Fig. 1d). Setting α750 reduced the mean pigment concentration for this

scene to 0.335 ± 1.134 mg m−3 (chlorophyll a + phaeopigments), and reduced the number of valid pixels to 48,203.

2.3.2 Effects of Surface Pressure Fields

The use of a spatially constant standard pressure field

of 1,013.25 mb (as used for the CZCS global processing),

rather than interpolated pressure fields, resulted in an increase in the computed mean pigment concentration for

scene 1. The magnitude of this increase varied as a function of the α750 value (Table 3). For example, setting

α750=1.6% and holding the pressure constant for the entire scene increased the calculated mean pigment concentration by 12%, from 0.601 to 0.671 mg m−3 (chlorophyll a

+ phaeopigments). (The value of 0.601 mg m−3 was obtained when pressure was allowed to vary spatially.) This

difference increased to 21% at α750 = 0.7%, which suggests that extra care must be taken in specifying surface

pressure at low albedo thresholds.

The choice of schemes used to apply surface pressure

to imagery had no impact on the number of pixels masked.

However, utilizing a spatially uniform pressure field rather

than a spatially variable one resulted in a higher number

of pixels with negative water-leaving radiances, which were

removed from further analysis. The difference in mean

pigment concentration for a given scene, resulting from

the choice of either a constant or variable pressure field,

was due to changes in pigment concentration for individual pixels only. Differences resulting from this choice were

greatest for pixels exhibiting relatively high pigment concentrations (Plate 3).

2.3.3 Surface Pressure Variation

A 2% change in the assigned surface pressure resulted

in marked differences in both spatially averaged pigment

concentrations and the number of valid pixels for scene 1

(Table 4). The default surface pressure used in the global

processing of CZCS imagery (1,013.25 mb) yielded an average pigment concentration of 0.406 ± 1.600 mg m−3 from

48,203 valid pixels. A total of 339 pixels had negative

water-leaving radiances at 520 or 550 nm, and were assigned a gray level of 254 and excluded. Increasing surface pressure to 1,033.25 mb increased the mean pigment

concentration to 0.625 ± 2.629 mg m−3, and decreased the

number of valid pixels to 47,584. This decrease in valid

pixels was due to an increase (to 958) in the number pixels

with negative water-leaving radiances at 520 or 550 nm.

Table 4. Spatially averaged pigment concentrations as a function of surface pressure for Scene 1.

All pigment concentrations have units of mg m−3.

Pressure Pigment Valid

[mb] Albedo Concentration Pixels

993.25 0.7% 0.306 ± 0.875 48,459

1,013.25 0.7% 0.406 ± 1.600 48,203

1,033.25 0.7% 0.625 ± 2.629 47,584

An increase in pigment concentration at higher pressures is due to the relationship between surface pressure

and Rayleigh optical thickness (τr). As surface pressure

increases, so does τr (McClain and Yeh 1994a). Because

the 443 nm band is more sensitive to changes in τr than

the 550 nm band, the 443:550 nm ratio decreases with increasing pressure, resulting in higher pigment estimates.

Reducing surface pressure to 993.25 mb lowered the mean

pigment concentration to 0.306 ± 0.875 mg m−3, and raised

the number of valid pixels to 48,459. This decrease in

pressure also reduced the number of pixels with negative

water-leaving radiances (at 520 or 550 nm) to 83.

2.3.4 High Reflectance Waters

Setting α750 to 1.6% in scene 2 (which is a highly reflective image characterized by the presence of a large coccolithophore bloom), resulted in an image containing numerous pixels with negative water-leaving radiances (Plate 4).

The low radiance values for these pixels presumably resulted from the presence of thin clouds that were not flagged

11
SeaWiFS Algorithms, Part 1

Fig. 2. Variations in the number of pixels having negative water-leaving radiances at 520 or 550 nm as a

function of α750 (water-leaving radiances at 443 nm were always positive). The numbers associated with each

point indicate the number of valid pixels, i.e., pixels with gray levels between 1–253, for each condition.

during routine level-2 processing. Reducing α750 from

1.6% to 0.7% masked all pixels with negative water radiances, while retaining more than 80% of the previously

valid ones (Plate 5). This observation suggests the use of a

reduced α750 threshold may even be appropriate for waters

that are considered to be highly reflective at visible wavelengths. A refined identification of coccolithophore blooms

can be accomplished using other algorithms (Brown 1995).

2.3.5 Sun Glint

Pixels affected by sun glint often have high reflectance

values at 750 nm, which may allow them to be flagged with

cloud masking algorithms. For example, in scene 3 (McClain and Yeh 1994b), many of the pixels affected by sun

glint were masked when α750 = 1.6%. The presence of sun

glint, however, resulted in numerous pixels with negative

water-leaving radiances at 520 and 550 nm, which were not

masked when processed with α750 = 1.6%. These pixels

were located mainly along the edge of the sun glint area

masked by the cloud flag, suggesting they were also contaminated. Figure 2 shows that most of the affected pixels

can also be masked by reducing α750, e.g., dropping α750

from 1.6% to 0.9% masked more than 99% of these pixels.

The number of pixels passing the cloud mask, however,

was also reduced, from 86,932 to 22,507.

2.4 CONCLUSIONS

These results show that adjusting the albedo threshold can markedly improve image quality. In most scenes,

more than 98% of cloud contaminated- and negative waterleaving radiance pixels were removed by reducing α750 from

1.6% to 0.7%. Performing similar analyses on more than

250 Southern Ocean scenes suggested that the best value

for α750 at high latitudes is approximately 0.7–0.9%. Although it is not apparent in the scenes shown, values for

α750 lower than 0.9%, in general, masked too many valid

pixels. The use of α750 values higher than 0.9% left too

many contaminated pixels unmasked.

Although pixels with negative water-leaving radiances

at 520 nm and 550 nm are flagged during level-2 processing, the flag consists of setting the pixel to the maximum

pigment concentration so that it can be easily identified.

This procedure can lead to problems, however, if the scene

contains pixels with genuinely high pigment values that

would be indistinguishable from the flagged ones. Masking these pixels with a cloud mask alleviates this problem.

The results presented here also imply that albedo-based

cloud masking algorithms are effective at removing pixels contaminated by the presence of sea ice and sun glint.

Anomalous pixels located at the pack ice interior, where

no obvious cloud cover was present, were eliminated along

with the cloud contaminated pixels (compare Plates 1 and

2). This result suggests that many of the scenes from high

latitudes, previously rejected in the CZCS global processing quality control program (McClain and Yeh 1994b) because of low solar elevations, may be salvaged by properly

applying masks.

12
C. McClain, K. Arrigo, W. Esaias, M. Darzi, F. Patt, R. Evans, J. Brown, C. Brown, R. Barnes, L. Kumar

Chapter 3

Classification of Coccolithophore Blooms

in Ocean Color Imagery

Christopher W. Brown

Goddard Space Flight Center

Greenbelt,Maryland

Abstract

Blooms of the coccolithophore Emiliania huxleyi, which occur in the surface layer of the ocean are characterized

by high ocean volume reflectance. This high reflectance biases the estimation of satellite-derived geophysical

parameters, e.g., pigment concentration, which are based on water-leaving radiance measurements. To avoid

erroneous estimates of geophysical parameters in the presence of blooms, it is desirable to implement a simple

classification algorithm within the SeaWiFS Data Processing System (SDPS) to flag their occurrence. The

development of a supervised, multispectral classification algorithm is described in this chapter. The algorithm

groups picture elements (pixels) of the imagery into coccolithophore bloom and non-bloom classes, based on the

normalized water-leaving radiances of each pixel.

3.1 INTRODUCTION

Blooms of the coccolithophore Emiliania huxleyi, occurring in the oceanic surface layer, possess a relatively

unique high ocean volume reflectance. Because of this optical property, such blooms have been observed in visible

satellite imagery from both temperate and subarctic latitudes (Holligan et al. 1983, Balch et al. 1991, Holligan et al.

1993, and Brown and Yoder 1994a and 1994b). The high

reflectance, caused principally by the presence of detached

coccoliths† (Ackleson and Holligan 1989 and Balch et al.

1991), profoundly affects the optical properties of the surface layer (Aiken et al. 1992 and Holligan et al. 1993). One

consequence of this condition is to bias satellite-derived estimates of pigment and chlorophyll a concentrations (Gordon et al. 1988b).

One component of the QC process planned for SeaWiFS imagery is to flag the occurrence of coccolithophore

blooms, because their presence will affect the accuracy of

estimated pigment concentration for that region. A simple classification algorithm has been developed to classify

coccolithophore blooms on a global scale in CZCS imagery

(Brown and Yoder 1994a). The algorithm, which is suitable to detect the presence of these blooms in the initial

† Coccoliths are microscopic calcareous plates which are produced by living coccolithophores to form a spherical framework called a coccosphere. Coccoliths of E. huxleyi are shed

continuously from the coccosphere during the organism’s life

cycle.

processing of SeaWiFS level-1b data, is outlined below.

This chapter briefly describes the method followed to establish the decision criteria used in the classification algorithm.

3.2 METHODS

The classification algorithm is based on the relatively

unique spectral signature of coccolithophore blooms, i.e.,

their high ocean volume reflectance in all of the CZCS

visible bands. The algorithm was developed by:

1) Empirically determining the spectral signatures

of coccolithophore blooms and various common,

non-bloom conditions;

2) Deciding upon feature characters (in this case

spectral) to be employed in the algorithm; and

3) Establishing decision boundary values for each

of the feature characters that would allow the

blooms to be spectrally distinguished from other

conditions.

The efficacy of the algorithm in separating the considered

conditions was also evaluated. The specific steps involved

in image processing, decision criteria development, and algorithm testing are presented below.

3.2.1 Image Processing

Images of normalized water-leaving radiances at three

wavelengths [LWN (440); LWN (520); and LWN (550)] and

13
SeaWiFS Algorithms, Part 1

aerosol radiance, La(670), from the NASA CZCS data set

(Feldman et al. 1989) were rectified to a cylindrical equidistant projection. The imagery had been atmospherically

corrected with a multiple scattering atmospheric correction

algorithm, using the default maritime aerosol epsilon values (Gordon et al. 1988a). Clouds and sun glint had been

masked, and scenes with excessively high aerosol radiance

or low sun angles were excluded during initial processing

at NASA.

3.2.2 Decision Criteria Development

The spectral signatures of several oceanic conditions—

coccolithophore blooms; clear blue water; whitings, i.e.,

suspended lime muds (Shinn et al. 1989); sediment-laden

water; and unmasked clouds (including cloud ringing)—

were ascertained by extracting normalized water-leaving

and aerosol radiances from pixels located at training sites

that were centered on portions of level-2 CZCS imagery

identified to correspond to each condition (Table 5 and

Fig. 3). Most of the training sites were located in the

North Atlantic. Those sites representing non-bloom conditions were often selected by contextual, though not necessarily verifiable, evidence. For example, training sites

for sediment-laden water were located at, or near, river

mouth locations. Sites used to identify whitings came

from the Bahamas and the Persian Gulf, areas which are

both known for this phenomenon (Robbins and Blackwelder 1992). The eight coccolithophore bloom training

sites were positioned in either the E. huxleyi bloom sampled by Holligan et al. (1983), or in the high reflectance

patches in, and adjacent to, the Gulf of Maine where E.

huxleyi blooms were sampled in subsequent years (Ackleson and Holligan 1989 and Balch et al. 1991).

The following five feature characters were employed in

the classification algorithm:

a) LWN (440);

b) LWN (550);

c) The ratio LWN (440)/LWN (520);

d) The ratio LWN (440)/LWN (550); and

e) The ratio LWN (520)/LWN (550).

The selection of these characters was based, in part, on

the suite of characters that proved least redundant in separating the spectral signatures of the various conditions,

as determined by examination of the covariance matrix,

and also those characters that explained the most variance

in stepwise discriminant analysis. The mean and standard

deviation for each feature character of the bloom, and for

all other spectrally-defined conditions with radiance values less than sensor saturation, i.e., less than 2.55, are

presented in Table 6.

Decision boundaries for each of the feature characters

(Table 7) were set to values that would exclude the greatest

percentage of non-bloom conditions, yet still include the

maximum percentage of blooms. An La(670) threshold

(1.10 mW cm−2 μm−1sr−1), which is slightly greater than

twice that of a clear atmosphere (Gordon et al. 1988b),

was also set to exclude atmospheric haze. (This threshold

is required because the global processing assumed the  ̊Angstr ̈om exponent was approximately zero, which is typical

of marine aerosols, but may not be true near continental

boundaries or air masses containing dust.)

The decision boundaries were then incorporated into

a non-parametric parallelepiped algorithm that assigned

non-land pixels to either the coccolithophore bloom class,

or the non-coccolithophore bloom class, by comparing the

radiance values of individual pixels to the decision boundaries set for each of the five spectral feature characters.

A parallelepiped algorithm classifies an object by comparing the object’s features to class (decision) boundaries

that form a parallelogram in two-dimensional feature space

(Schowengerdt 1983). The algorithm was structured in a

manner that was considered to be the most efficient assuming reasonable expectations of the frequency of the various

conditions in the oceans (Fig. 4). For example, low reflectance water at LWN (550)—representing, presumably,

the most common condition in the ocean—was dismissed

in the first step.

3.2.3 Algorithm Performance

To evaluate the performance of the decision boundaries

in separating the different classes, test site pixels (Table 5)

were classified, and the percentage of correct and incorrect

classifications were calculated based on their previous assignment. As with training site pixels, test site pixels provided a representative spectral signature for each of the

various conditions, however, their radiances were not consulted when establishing the decision boundaries.

The effect of the differences in spatial resolution between the training pixels (4 km) and the classified postage

stamp (PST†) 20 km pixels (Feldman et al. 1989), was assessed using a two-step procedure, in order to evaluate

algorithm performance on a global scale. This procedure

initially calculated 500 mean radiance values for each condition from 2n randomly chosen test site pixels (where n

ranged from 0–6), and then classified these simulated observations. For these simulations, it was assumed that pixels binned into a PST image represented a random sampling of radiances from a given condition.

3.3 CLASSIFICATION RESULTS

The simulations used to evaluate the efficacy of the

classifier revealed that both the number of omissions (i.e.,

the test bloom pixels excluded during classification from

the bloom class) and the number of commissions (i.e., the

non-bloom test pixels incorrectly classified into the bloom

class), generally decreased as the spatial resolution of the

† PST is a designation for compressed files.

14
C. McClain, K. Arrigo, W. Esaias, M. Darzi, F. Patt, R. Evans, J. Brown, C. Brown, R. Barnes, L. Kumar

Table 5. Center coordinates and dates for the training and test sites used to establish spectral signatures

for coccolithophore bloom and non-coccolithophore bloom conditions. The water conditions or water mass

type are designated as follows: Clear, clear water; Coccolithophore, coccolithophore bloom; Haze, aerosol haze;

Sediments, suspended sediments; Red, red water condition; and Whitings, whitings. Latitude and longitude are

in decimal degrees. For the date field, YY is the decadal year and DDD is the sequential day of the year.

Date Water Training Site Testing Site

YY DDD Condition Latitude Longitude Code Latitude Longitude Code

83 153 Clear 39.54◦N 70.81◦W 3 38.48◦N 71.05◦W 3

83 180 Clear 43.49◦N 66.43◦W 1 46.59◦N 58.72◦W 2

83 181 Clear 42.75◦N 63.96◦W 2 42.48◦N 62.05◦W 2

83 182 Clear 43.49◦N 60.13◦W 2 42.48◦N 62.05◦W 2

83 191 Clear 41.03◦N 64.24◦W 2 40.52◦N 65.10◦W 3

79 188 Coccolithophore 42.40◦N 64.28◦W 2 42.55◦N 63.69◦W 2

82 147 Coccolithophore 48.25◦N 8.45◦W 5 48.13◦N 6.85◦W 5

82 148 Coccolithophore 48.25◦N 8.45◦W 5 48.13◦N 6.85◦W 5

82 149 Coccolithophore 48.29◦N 8.53◦W 5 48.13◦N 6.92◦W 5

83 153 Coccolithophore 42.24◦N 68.93◦W 1 41.54◦N 68.39◦W 1

83 160 Coccolithophore 42.59◦N 69.13◦W 1 43.14◦N 69.44◦W 1

83 180 Coccolithophore 43.14◦N 69.29◦W 1

Coccolithophore 42.75◦N 68.29◦W 1

83 181 Coccolithophore 42.44◦N 69.01◦W 1 42.36◦N 67.84◦W 1

Coccolithophore 41.89◦N 69.05◦W 1

Coccolithophore 42.63◦N 67.33◦W 1

83 182 Coccolithophore 42.01◦N 68.93◦W 1 42.16◦N 68.15◦W 1

Coccolithophore 42.52◦N 68.35◦W 1

83 194 Haze 42.16◦N 69.25◦W 1 43.61◦N 63.65◦W 2

83 210 Haze 41.61◦N 69.25◦W 3 40.71◦N 70.19◦W 3

85 247 Red 39.07◦N 73.63◦W 3 39.58◦N 73.75◦W 3

79 125 Sediments 6.24◦N 53.85◦W 6 5.81◦N 53.50◦W 6

79 164 Sediments 1.94◦N 48.69◦W 6 5.81◦N 53.03◦W 6

79 177 Sediments 48.54◦N 68.70◦W 4 47.76◦N 69.76◦W 4

79 185 Sediments 47.76◦N 69.76◦W 4

79 202 Sediments 5.58◦N 52.68◦W 6 5.89◦N 53.58◦W 6

Sediments 5.66◦N 52.91◦W 6

79 208 Sediments 5.77◦N 53.30◦W 6 6.20◦N 56.24◦W 6

Sediments 0.22◦S 47.82◦W 6 5.97◦N 53.54◦W 6

79 208 Sediments 6.01◦N 56.16◦W 6 5.03◦N 52.09◦W 6

85 273 Sediments 41.11◦N 70.62◦W 3 41.22◦N 71.05◦W 6

79 004 Whitings 28.15◦N 50.82◦E 7 27.89◦N 49.27◦E 7

79 015 Whitings 27.78◦N 50.68◦E 7 24.51◦N 53.95◦E 7

79 028 Whitings 28.09◦N 50.05◦E 7 26.39◦N 50.78◦E 7

79 032 Whitings 28.95◦N 50.21◦E 7 26.39◦N 50.78◦E 7

80 023 Whitings 21.15◦N 71.67◦W 8

80 024 Whitings 21.15◦N 71.67◦W 8

1 Gulf of Maine, western North Atlantic.

2 Nova Scotia Shelf or Slope, western North Atlantic.

3 New York Bight or Slope Water off the northeastern US, western North Atlantic.

4 St. Lawrence River, western North Atlantic.

5 Celtic Shelf, eastern North Atlantic.

6 Coastal waters of northeastern South America, equatorial North Atlantic.

7 Persian Gulf.

8 Bahamas, equatorial North Atlantic.

15
SeaWiFS Algorithms, Part 1

Table 6. Spectral statistics (mean ± standard deviation) from training sites of the environmental conditions

when CZCS band 3 (550 nm) was not saturated. The water types are designated by the following codes:

Clear, clear water; Coccolithophore, coccolithophore bloom; Haze, atmospheric haze; Red, red water condition;

Sediments sediments; Outflow, river outflow; and Whitings, whitings. LW N (λ) represents the normalized waterleaving radiance, with units of mW cm−1 μm−1 sr−1 (from Brown and Yoder 1994a).

Water Number LWN (440) LWN (550) LWN (440)/ LWN (440)/ LWN (520)/

Condition of Images LWN (520) LWN (550) LWN (550)

Clear 1,435 0.80 ± 0.20 0.42 ± 0.87 1.4 ± 0.30 2.0 ± 0.50 1.4 ± 0.19

Coccolithophore 1,095 1.92 ± 0.41 1.43 ± 0.42 1.1 ± 0.14 1.4 ± 0.27 1.3 ± 0.11

Haze 360 1.95 ± 0.44 0.99 ± 0.07 1.5 ± 0.29 2.0 ± 0.43 1.3 ± 0.07

Red 180 1.08 ± 0.15 1.24 ± 0.08 1.0 ± 0.11 0.9 ± 0.13 0.9 ± 0.06

Sediments 1,189 1.17 ± 0.48 1.92 ± 0.39 0.6 ± 0.20 0.6 ± 0.27 1.0 ± 0.15

Outflow 136 0.40 ± 0.26 0.82 ± 0.19 0.5 ± 0.16 0.5 ± 0.41 1.1 ± 0.61

Whitings 838 2.06 ± 0.35 1.40 ± 0.43 1.2 ± 0.33 1.6 ± 0.67 1.3 ± 0.14

Fig. 3. Mean (x + standard deviation) normalized water-leaving radiances for coccolithophore blooms (•),

clear blue water (◦), haze (), sediments (), and whitings () at the band wavelengths of the CZCS (from

Brown and Yoder 1994a).

16
C. McClain, K. Arrigo, W. Esaias, M. Darzi, F. Patt, R. Evans, J. Brown, C. Brown, R. Barnes, L. Kumar

Fig. 4. Flow diagram illustrating the coccolithophore bloom detection algorithm developed for use with CZCS

global processing data products.

17
SeaWiFS Algorithms, Part 1

Fig. 5. An evaluation of classification performance at different spatial resolutions. The spectral signatures of

bloom and non-bloom conditions at different pixel spatial resolutions were simulated by increasing the sample

size (n is equal to 2n) of pixels used to compute the mean radiance value of a single PST pixel. The mean

number of level-2b pixels comprising all valid PST pixels in this study, i.e., 18, is denoted by the vertical line

at X. Omissions indicate the percentage of test bloom pixels excluded from the bloom class after classification

and commissions indicated the percentage of non-bloom pixels incorrectly included in the classified bloom class.

These results do not include the testing of whitings (Brown and Yoder 1994a).

imagery decreased (Fig. 5). The combined percentage of

commissions and omissions decreased to less than 3% when

the simulated sample size (2n) was 16, equivalent to a spatial resolution of 16 km2 (Fig. 5). Note that an average of

18 level-2 pixels (out of a nominal maximum of 25) were

binned to compute the mean radiance of a single 5-day

PST pixel (Brown and Yoder 1994a). These simulation

results suggest, therefore, that most pixels are accurately

classified at this low spatial resolution. At higher spatial

resolutions, however, the percentage of omissions and commissions increases. This finding reveals that the algorithm

is not accurate for higher resolution CZCS imagery, i.e.,

level-2, in regions where conditions with spectral signatures, which mimic coccolithophore blooms, exist.

Whitings, sediment, and atmospheric haze were the

only non-bloom conditions misclassified as blooms during

the testing simulations. Whitings were spectrally indistinguishable from coccolithophore blooms using the present

classification scheme. Caution must be taken, therefore,

when assigning the cause of a flagged pixel, particularly at

lower latitudes where these conditions are most predominant.

Examples of the results from the detection algorithm

are illustrated in Plate 6. The top left and top right images in Plate 6 are true-color composites of CZCS images

that show coccolithophore blooms located in the North Atlantic Ocean south of Iceland, and on the northeastern US

continental shelf, respectively. The image of the Icelandic

bloom (orbit 8876) was processed using the default atmos18
C. McClain, K. Arrigo, W. Esaias, M. Darzi, F. Patt, R. Evans, J. Brown, C. Brown, R. Barnes, L. Kumar

pheric correction epsilon coefficients. The image of the US

continental shelf (orbit 3171), which is also presented in

Gordon et al. (1983), was processed manually to remove

a significant aerosol signal ( ̊Angstr ̈om exponent = 0.6).

The blooms appear milky white to turquoise in color, and

clouds are masked black.

The coccolithophore flag masks of these images (Plate 6,

bottom left and bottom right) reveal the effectiveness of

the algorithm in both the open ocean and the more complex coastal environment. Coccolithophore blooms are colored white and non-bloom conditions are colored blue.

Note in the top right image that the relatively high reflectant yellow waters located just south of Cape Cod (the

prominent U-shaped peninsula) were not grouped into the

bloom class (bottom right).

Table 7. Decision boundary values of the feature

characters used at the global and regional scale. Radiance values are given in units of mW cm−1 μm−1

sr−1.

Feature Character Global Value

LWN (440) 1.10 <x< 2.55

LWN (550) 0.80 ≤ x < 2.55

LWN (440)

LWN (520) 0.95 ≤ x ≤ 1.50

LWN (440)

LWN (550) 1.00 <x< 2.00

LWN (520)

LWN (550) 1.00 ≤ x ≤ 1.60

The results of the algorithm indicate that its performance is quite good in the higher latitudes, based on comparisons with what was previously known of the distribution pattern of coccolithophore blooms and other potentially mimicking conditions (Brown and Yoder 1994a).

At the lower latitudes, however, discrimination of high reflectance signals, caused by coccolithophore blooms from

other sources of high reflectance, will likely remain problematic.

3.4 DISCUSSION

The detection of coccolithophore blooms is sensitive to

light backscattered from approximately one attenuation

depth in the water column, and is primarily a function

of coccolith, not cell, concentration (Balch et al. 1991).

The detection of coccolithophore blooms occurring in the

surface layer is consequently biased toward the declining

stage, i.e., stationary phase, of the bloom, when the proportion of coccolith-to-cell concentration is greatest (Balch

et al. 1991). It is presumed that all patches recognized as

blooms are composed of the coccolithophore E. huxleyi, the

only species presently documented to be visible in satellite

imagery (Balch et al. 1991 and Holligan and Balch 1991).

The coccolithophore bloom detection algorithm described above is a simple and intuitive approach based on

the empirically derived spectral signatures of coccolithophore bloom and non-bloom conditions. The ability of

the algorithm to detect blooms will most likely improve

when used with SeaWIFS imagery, due to the planned improvement in atmospheric correction and cloud masking

procedures.

Acknowledgments

Drs. E-n. Yeh and C.R. McClain provided the processed CZCS

images and commented on the text.

19
SeaWiFS Algorithms, Part 1

Chapter 4

A Proposed On-Orbit, Out-of-Band

Correction Scheme for SeaWiFS

Robert A. Barnes

ManTech Environmental Technology,Inc.

Wallops Island,Virginia

Wayne E. Esaias

Charles R. McClain

Goddard Space Flight Center

Greenbelt,Maryland

Abstract

Out-of-band responses for the eight SeaWiFS bands are elements of the instrument’s radiometric calibration.

In that calibration, the instrument views a broad area of known radiance, and the output from the bands are

recorded in counts. The counts from each band include the out-of-band contribution, and these out-of-band

contributions are functions of the spectral shape of the source that is measured. The SeaWiFS laboratory

calibration, therefore, has the out-of-band correction for a 2,850 K source factored into its results. If the

instrument measures a source with that particular spectral shape, those measurements automatically contain

appropriate out-of-band corrections. The prelaunch calibration equations for SeaWiFS contain correction terms

that convert the out-of-band responses from those for a 2,850 K source to those for a 5,900 K source. As a

result, the SeaWiFS calibration equations now have the out-of-band correction for a 5,900 K source factored

into them. The 5,900 K spectral shape closely duplicates the spectral shape for SeaWiFS ocean measurements.

The errors arising from the use of the 5,900 K out-of-band corrections for ocean measurements are estimated to

be small, on the order of a few tenths of a percent. If an alternate out-of-band correction is to be used, then the

5,900 K correction must be removed from the measurement results and a new out-of-band correction inserted in

its place. An out-of-band correction based on the actual measurements from SeaWiFS, plus the procedure for

its implementation, is presented here.

4.1 SPECIFICATION

The specifications of the Ocean Color Data Mission

(OCDM) contract call for an out-of-band response that is

less than 5% of the within-band value (Barnes et al. 1994a).

For this specification, within-band is defined as the wavelength interval between the upper and lower 1% response

points. The within-band response is the integrated instrument response between these points. Out-of-band includes

everything else, both above and below the 1% response

points. The 1% response points are also called the upper

and lower extended band edges. The wavelengths for these

edges can be found in Table 13 of Barnes et al. (1994b),

which is reproduced as Table A-1 in Appendix A of this

document.

The specifications call for the out-of-band response to

be determined for a radiant source with a spectral shape

equivalent to the sun. For practical purposes, including

out-of-band calculations, the sun can be considered as a

5,900 K blackbody (Allen 1973 and Warneck 1988).

Figure 6 shows the spectral shape of two blackbodies

(5,900 K and 2,850 K). One has the spectral shape of the

solar output and the other the spectral shape of the light

source used in the laboratory. These blackbody curves

have been normalized to the typical input radiance for

SeaWiFS band 1, as given in the performance specifications. For a 5,900 K source, an out-of-band response, i.e.,

a light leak, near 500 nm will be emphasized, relative to a

leak near 1000 nm. For a 2,850 K source, however, the reverse is true. The out-of-band portion of a SeaWiFS measurement, therefore, includes more than the wavelengthdependent response of the instrument—it also includes the

wavelength-dependent shape of the radiance measured by

the instrument.

To facilitate out-of-band calculations, the response of

the instrument (called the system response) to a spectrally

20
C. McClain, K. Arrigo, W. Esaias, M. Darzi, F. Patt, R. Evans, J. Brown, C. Brown, R. Barnes, L. Kumar

Fig. 6. Blackbody radiance curves normalized to the typical radiance at 412 nm from the SeaWiFS specification.

The top panel shows the curve for a 5,900 K blackbody that approximates the solar spectrum. The bottom panel

shows the curve for a 2,850 K blackbody that approximates the SBRC integrating sphere.

21
SeaWiFS Algorithms, Part 1

flat light source, i.e., to a light source with a radiance of

1 mW cm−2μ m−1sr−1, was calculated at each wavelength

from 380–1,150 nm. Barnes et al. (1994b) details the development of this response calculation. Figure 7 shows this

system response curve for SeaWiFS band 1. This response

has been calculated from measurements of the transmittances and reflectances of the optical components for the

band, and from the response of the photodiode. The output for the band in Fig. 7 is given as nanoamperes (nA)

from the photodiode at each 1 nm interval for the measurements of the spectrally flat source.

The radiance sources in Fig. 6 are given as mW of radiance at each 1 nm interval. It is a straightforward calculation to multiply the instrument response and the radiance

at each 1 nm interval to give the response of the instrument

to the radiant source. Such calculations were made for the

response of each SeaWiFS band to a 5,900 K blackbody

source in Table 14 of Barnes et al. (1994b), (reproduced as

Table A-2 in Appendix A). The calculations in that table

include the integrated values for the lower out-of-band response (from 380 nm to the lower extended band edge), for

the in-band response (from the lower to the upper extended

band edge), and for the upper out-of-band response (from

the upper extended band edge to 1,150 nm). The percent

out-of-band response is calculated as the sum of the individual out-of-band responses divided by the in-band response. It is important to note that in these calculations

the absolute values for the light intensity from the radiant

source are not important. It is the wavelength-dependent

shape (in relative terms) for the source that is important.

The percent out-of-band response is calculated as a ratio.

4.2 OUT-OF-BAND CORRECTION

The percent out-of-band responses in Table A-2 are

given for a source with a 5,900 K blackbody shape. This

source approximates the spectral shape for the output of

the sun. SeaWiFS, however, will measure upwelling radiances from the Earth, radiances with a spectral shape

different from that for the sun. It is proposed here that

the on-orbit SeaWiFS measurements can be used to provide the spectral shape for the radiant source in the outof-band calculations.

Figure 8 gives the typical input radiances for the eight

SeaWiFS bands. These radiances have been taken from

the specifications for the instrument. The radiances have

been connected by lines such that the radiances at the intervening wavelengths can be calculated by linear interpolation. The radiance at 380 nm has been arbitrarily made

the same as that for SeaWiFS band 1 at 412 nm. In the

same manner, the radiance value at 1,150 nm has been set

to one-third of the radiance value for SeaWiFS band 8 at

865 nm. Such extensions must be introduced since the SeaWiFS bands do not completely cover the wavelength range

from 380–1,150 nm.

There are significant reasons behind the selection of the

end-point values given above. In Fig. 9, the typical SeaWiFS radiances have been plotted against three blackbody

curves: 10,000 K, 12,000 K, and 14,000 K. These curves are

hotter, and therefore bluer, than the 5,900 K solar spectrum. The three curves have each been normalized to give

the typical radiance value for SeaWiFS band 1 at 412 nm.

The typical SeaWiFS radiances in Fig. 9 approximate the

spectral shape of blue light upwelling from the atmosphere,

that is, of light dominated by Rayleigh scattering. For

most SeaWiFS ocean measurements, the upwelling radiance from the Earth is dominated by the atmosphere.

In Fig. 9, the radiance from the three curves is greater

at 380 nm than at 412 nm. For the 5,900 K blackbody curve

in Fig. 6, the value at 380 nm is less than that at 412 nm.

It seemed appropriate to split this difference and to set

the 380 nm value in the on-orbit radiance spectrum equal

to that at 412 nm. This setting represents an extrapolation of about 15 nm in the reference spectrum, since the

lower extended band edge for SeaWiFS band 1 is located

at about 395 nm.

For the upper end of the reference spectrum, 1,150 nm,

the extrapolation is about 240 nm, since the upper extended band edge for SeaWiFS band 8 is at about 910 nm.

In this case, however, the four reference curves (the three

curves in Fig. 9 and the 5,900 K curve in Fig. 6) all give values at 1,150 nm that are significantly lower than the value

at 865 nm. From the curves in Fig. 9, it was determined

that a reduction in radiance from 865–1,150 nm by a factor

of three seemed appropriate.

4.3 IMPLEMENTATION

For a SeaWiFS pixel of Earth-exiting radiance, it is

possible to calculate a set of radiances from 380–1,150 nm

as presented above. Using the spectrally flat instrument

response spectra from Section 4.1, it is possible to perform a point-by-point multiplication to obtain the output

at each wavelength from 380–1,150 nm. Using the lower

and upper extended band edges in Table A-2, the percent

out-of-band response for the Earth-exiting radiance can

be calculated. The fractional out-of-band response for a

5,900 K blackbody (see Table A-2) can be removed from

the measurements, and the fractional correction for the

Earth-exiting radiance can be applied in its place.

As this procedure is developed by the researcher, it is

important to bear in mind that the out-of-band response

is an integral part of the laboratory calibration of the instrument. The out-of-band counts from the instrument

are part of the total counts that SeaWiFS produces when

exposed to the laboratory radiance source. Earth-exiting

radiance will generate a different number of counts from

the out-of-band portion of the spectrum. This difference

in counts must be reassessed from pixel to pixel if the measurements are to be made with the greatest accuracy.

22
C. McClain, K. Arrigo, W. Esaias, M. Darzi, F. Patt, R. Evans, J. Brown, C. Brown, R. Barnes, L. Kumar

Fig. 7. System response curve for SeaWiFS band 1. This data includes the optical components and the

photodiode response to a spectrally flat light source with a radiance of 1 mW cm−2 sr−1 μm−1. The top panel

shows the system response on a linear scale. The out-of-band response near 500 nm is barely noticeable. The

bottom panel illustrates the system response on a logarithmic scale.

23
SeaWiFS Algorithms, Part 1

Fig. 8. A spectrum of upwelling Earth radiance based on the SeaWiFS typical input radiances. Radiance

curves of this type, using actual SeaWiFS measurements, are proposed for the on-orbit out-of-band correction

calculations.

Fig. 9. Typical input radiances from SeaWiFS are plotted with three blackbody radiance curves. The high

temperatures of the radiance curves indicate a radiance source that is more blue than the sun, i.e., the sources

have more emission in the blue portion of the visible spectrum.

24
C. McClain, K. Arrigo, W. Esaias, M. Darzi, F. Patt, R. Evans, J. Brown, C. Brown, R. Barnes, L. Kumar

There is a second technique for converting the out-ofband contribution to the laboratory calibration. This involves the calculation of the sum over the wavelength range

from 380–1,150 nm for each band and for each source’s

spectral shape (Barnes et al. 1994b). The correction can be

applied using the ratios of these integrals. There is a normalization process required in this second method, however, since the integral values for the 2,850 K and 5,900 K

results have been calculated using the saturation radiance

for each band at its nominal center wavelength. These values are given in Table 11 of Barnes et al. 1994a. These

radiances are:

a) 13.63 mW, at 412 nm, for band 1;

b) 13.25 mW, at 443 nm, for band 2;

c) 10.50 mW, at 490 nm, for band 3;

d) 9.08 mW, at 510 nm, for band 4;

e) 7.44 mW, at 555 nm, for band 5;

f) 4.20 mW, at 670 nm, for band 6;

g) 3.00 mW, at 765 nm, for band 7; and

h) 2.13 mW, at 865 nm, for band 8.

The normalization procedure for the integrals is necessary

because the Earth-exiting radiance measured for each band

on orbit will not have the corresponding value listed above

at the band’s central wavelength.

4.4 CONCLUDING REMARKS

As shown in Table A-1, the out-of-band responses for

the eight SeaWiFS bands range from about 0.3% to about

3.7%. These responses were calculated for a 5,900 K blackbody source, a source that duplicates the solar spectrum.

The 5,900 K source approximates the spectral shape of

the upwelling Earth radiance for SeaWiFS ocean measurements. The actual measurements made by SeaWiFS on

orbit will, however, give an improved spectral shape for

the upwelling radiance. The authors have proposed to use

the actual measurements at the eight SeaWiFS bands (412,

443, 490, 510, 555, 670, 765, and 865 nm) plus two calculated values (at 380 and 1,150 nm) to form the radiance

spectrum for the out-of-band calculation. The value at

380 nm is set equal to that at 412 nm, and the value at

1,150 nm is set to one-third the value at 865 nm.

Although an extensive error analysis has not been performed, it is thought that an out-of-band correction using

this instrument-based spectrum should provide a calculated out-of-band amount that is good to within a few

tenths of a percent.

4.5 DATA ACCESS

Files that contain the response of the SeaWiFS bands

are available from GSFC. There are three American Standard Code for Information Interchange (ASCII) files. The

first, SPECREAD.ME, gives an explanation of the two files

that contain the spectral response data. The second,

SPECTRA1.DAT, gives the response values for SeaWiFS

bands 1–4. The third file, SPECTRA2.DAT, gives the response values for SeaWiFS bands 5–8. It is currently possible to obtain the information through the Goddard Distributed Active Archive Center (DAAC). For further information about access to the DAAC data, contact:

DAAC/User Services Office

NASA/GSFC/Code 902.2

Greenbelt, MD 20771

Voice: 301–286–3209

Fax: 301–286–1775

Net: daacuso@daac.gsfc.nasa.gov

25
SeaWiFS Algorithms, Part 1

Chapter 5

Algorithm for the Application of the Sensor

Calibration for SeaWiFS Level-2 Processing

Michael Darzi

Frederick S. Patt

General Sciences Corporation

Laurel,Maryland

Lakshmi Kumar

Hughes STX

Lanham,Maryland

Abstract

The processing of SeaWiFS level-1 data to level-2 requires that the sensor calibration be applied to the raw

counts, prior to the derivation of geophysical values. The algorithm described herein is the implementation

of the sensor calibration approach of Barnes et al. (1994b). The algorithm is designed for operational use by

the SeaWiFS Project, and works on individual scan lines of SeaWiFS level-1a data, returning sensor-calibrated

radiance values (level-1b data). Calibration data used in the calculations are stored in the sensor calibration

table, which is also described.

5.1 INTRODUCTION

This document describes the algorithms of two SeaWiFS operational routines, calibrate_l1a and get_cal.

These routines are used to apply the sensor calibration

to level-1a data during the generation of level-2 products

(Fig. 10). The algorithms implement the sensor calibration

approach described in Barnes et al. (1994b). The calibration data are stored in the sensor calibration table, which

is also described in this document.

5.2 THE calibrate l1a ALGORITHM

The processing of level-1 data to level-2 requires that

the sensor calibration be applied to the raw counts, prior

to the derivation of geophysical values. The level-1a data

calibration routine, calibrate_l1a, takes an array of raw

counts (level-1a data) and, after applying the sensor calibration, returns a corresponding array of radiance values

(level-1b data). It is designed to work on part, or all,

of a scan line at a time. The interface specifications for

this routine are given in an interface specifications document written for the SeaWiFS Project and authored by M.

Darzi.

5.2.1 Variables

The following subsections define the variables used in

the pseudocode of the calibrate_l1a algorithm (5.2.2).

5.2.1.1 Input Arguments

The following variables provide data required by

calibrate_l1a and, therefore, comprise its input arguments as passed by the level-2 processing program:

cal_path (character): the directory path and filename for the sensor calibration table file (used by

get_cal);

syear (2-byte integer): the year of the scene start

time (used by get_cal);

sday (2-byte integer): the day of the year for the

scene start time (used by get_cal);

smsec (4-byte real): the milliseconds of the day for

the scene start time (used by get_cal).

eday (2-byte integer): the day of the year for the

scene end time (used by get_cal);

msec (4-byte real): the scan line time in milliseconds

of the day (used by get_cal);

dtype (character): the data type flag (used by

get_cal);

st_samp (4-byte integer): the number (one-relative,

i.e., relative to an initial value of one) of the first

sample to process on each scan line;

nsamp (4-byte integer): the number of samples to

process in each scan line;

26
C. McClain, K. Arrigo, W. Esaias, M. Darzi, F. Patt, R. Evans, J. Brown, C. Brown, R. Barnes, L. Kumar

Fig. 10. Flow chart of the SeaWiFS operational sensor calibration procedure, showing idealized level-2 product

generation steps, and the algorithms for the routines calibrate l1a and get cal, which are described in greater

detail in the text.

dark_rest (2-byte integer, array size 8): the dark

restore (zero offset) for l1a_data values for all eight

bands;

gain (2-byte integer, array size 8): the gain values

(0–3) for all eight bands;

tdi (2-byte integer, array size 8): the time delay

and integration (TDI) values for all eight bands

(used by get_cal);

scan_temp (2-byte integer, array size 8): the digitized scan temperatures for all eight bands;

side (2-byte integer): the mirror side (0 or 1) of

the scan line; and

l1a_data (2-byte integer, array size 8×nsamp): the

scan line of raw counts for all eight bands.

5.2.1.2 Output Arguments

The calibrated radiance values, and the date of entry

to the calibration table, are returned by calibrate_l1a

to the level-2 processing program:

• cal_year (2-byte integer): the year (4 digits) of

each entry (i.e., update) to the calibration table, as

returned by get_cal;

• cal_day (2-byte integer): the day of the year of

each entry (i.e., update) to the calibration table, as

returned by get_cal; and

• l1b_data (4-byte real, array size 8 × nsamp): the

sensor calibrated radiance values corresponding to

l1a_data.

5.2.1.3 Variables Returned By get cal

The following variables are returned by get_cal for use

by calibrate_l1a:

 cal_year (2-byte integer): the year (4 digits) of the

entry (i.e., update) to the calibration table;

 cal_day (2-byte integer): the day of the year for

the entry (i.e., update) to the calibration table;

 temps (4-byte real, array size 256 × 8): the temperature correction coefficients;

 scan_mod (4-byte real, array size 2 × 1,285): the

scan modulation correction factors (the array dimensions represent even/odd band number × pixels); if dtype = GAC, then only the first 248 values

of the pixels dimension are used;

 mirror (4-byte real, array size 2 × 8): the correction factors for mirror side 0 and mirror side 1 (the

27
SeaWiFS Algorithms, Part 1

dimension is computed from the number of sides

times the number of bands);

 time_factor(4-byte real, array size 8): the timedependent correction factors for all bands;

 counts (4-byte real, array size 8 × 4 × 5): the

digital counts (zero-offsets corrected) corresponding to each calibration knee for all gains and bands

(the dimension is computed from the number of

bands times the number of gains times the number

of knees); and

 rads (4-byte real, array size 8 × 4 × 5): the radiances corresponding to each calibration knee for all

gains and bands (the dimension is computed from

the number of bands times the number of gains

times the number of knees).

5.2.1.4 Internal Variables

The following variables are used for processing within

calibrate_l1a:

called_get_cal (logical): used to check if get_cal

has been called;

first_call (logical): used to check if it is the first

call to this routine (the initial value is TRUE);

band (2-byte integer): an index for looping through

bands;

knee (2-byte integer): an index for looping through

the knees of the bilinear gains;

n (2-byte integer): a temporary counting variable;

count1 and count2 (2-byte integers): used for the

temporary storage of radiance counts;

slope (4-byte real): used for temporary storage of

the slope value from the function used to convert a

level-1a count to radiance;

count (2-byte integer): an index for looping through

the possible radiance count values;

g_f (4-byte real; array size 8 × 1024): the values in

the look-up table (LUT) containing the gain factors

for a given gain for all count values and all eight

bands;

pixel (2-byte integer): an index for looping through

the pixels in a scan line; and

l1_data (2-byte integer): used for the temporary

storage of a dark restore-corrected count.

5.2.2 Algorithm

This section presents the calibrate_l1a algorithm using a combination of pseudocode and associated narrative

comments.

5.2.2.1 Initialization

The routine calibrate_l1a calls get_cal to get the

calibration parameters (corresponding to the date and time

of the data and the TDI) if any of the following three

situations occur:

1. It is the first call to calibrate_l1a.

2. The scene start date or start time has changed from

the previous call to calibrate_l1a, indicating a

new scene. In this case, the new scene may belong to

a different time range in the sensor calibration table.

[Note get_cal is not called if the scan line time

variable (msec) changes, since it will change with

every scan line; thus, some data for a scene located

at the limits of a range will extend into another

entry’s time range. In effect, this logic assumes the

sensor calibration does not change during a scene.]

3. The TDI has changed. In this case, new values

of counts and rads, which are functions of TDI,

must be obtained. The other parameters will also

be reread, even though they do not change.

The following logic is for calling get_cal:

called_get_cal = FALSE

if (first_call) or (syear or sday or smsec

or tdi has changed from previous call)

then

first_call = FALSE

save syear, sday, smsec, and tdi for next call

call get_cal:get cal_year, cal_day, temps, scan_mod,

mirror, time_factor, counts, rads

called_get_cal = TRUE

end if

The following logic sets up the gain factor LUT. This

procedure is done to avoid the interpolation that would

otherwise be needed with each pixel for the bilinear gains.

The do while loop accounts for knees having the same saturation counts, which results from certain TDI combinations with duplicate detectors.

if (called_get_cal) or (gain has changed

from previous call) then

save gain for next call

do band = 0 to 7

do knee = 1 to 4

n=1

do while ((counts(band,gain(band),knee)=

counts(band,gain(band),knee-n)) and

(n=<k)

n=n+1

end do while

count1 = counts(band,gain(band),knee-n)

count2 = counts(band,gain(band),knee)

if knee = 4, count2 = 1023

slope = (rads(band,gain(band),knee)-

rads(band,gain(band),knee-n))/

(counts(band,gain(band),knee)-

counts(band,gain(band),knee-n))

do count = count1 to count2

g_f(band,count) = slope *

(count-counts(band,gain(band),knee-n))+

rads(band,gain(band),knee-n)

end do

end do

end do

end if

28
C. McClain, K. Arrigo, W. Esaias, M. Darzi, F. Patt, R. Evans, J. Brown, C. Brown, R. Barnes, L. Kumar

5.2.2.2 Application of Sensor Calibration

The highest looping structure cycles through the eight

bands.

do for band = 0 to 7

The next loop cycles through the pixels of the scan line.

Note that although st_samp and nsamp need not represent

an entire scan line of pixels, the pixels must be consecutive

pixels of a scan line because the scan modulation correction

factor array assumes such an arrangement.

do for pixel = st_samp-1 to st_samp+nsamp-2

The following logic is to correct for dark restore. Note that

since the temporary value l1_data is used as an index, its

value is forced to lie between 0–1,023, to account for the

possibility that either the dark restore of the scan line is

greater than l1a_data, or that the raw count values are

corrupted.

l1_data = l1a_data(band,pixel) - dark_rest(band)

if l1_data < 0, l1_data = 0

if l1_data > 1023, l1_data = 1023

This line of code retrieves the corresponding radiance from

the gain factor LUT.

l1b_data(band,pixel) = g_f(band,l1_data)

First, the scan modulation correction is applied. The modular arithmetic function (mod) is used to determine if the

even- or odd-band correction values should be used.

l1b_data(band,pixel) =

l1b_data(band,pixel) * scan_mod(mod(band,2),pixel)

Next, the time-dependent correction is applied.

lib_data(band,pixel) =

l1b_data(band,pixel) * time_factor(band)

Then the temperature correction is applied, using the byte

scan_temp value as an index to look up the temperature

correction factor.

l1b_data(band,pixel) =

l1b_data(band,pixel)*temps(scan_temp(band),band))

Finally, the mirror correction factor, for the mirror side

corresponding to the scan line, is applied.

l1b_data(band,pixel) =

l1b_data(band,pixel) * mirror(side,band)

end do

end do

return

5.3 THE get cal ALGORITHM

The sensor calibration table is accessed operationally

during the generation of level-2 products. The sensor calibration table read routine, get_cal, will open the sensor

calibration table file, retrieve the sensor calibration data,

and close the file during each call. The routine will return, for specified time and TDI values, the breakpoints

(which are also called knees) in the band response function for each band and gain. The routine also returns the

temperature and mirror side correction terms. The interface specifications for this routine are given in the interface

specifications document authored by M. Darzi.

5.3.1 Variables

The following subsections define the variables used in

the pseudocode of the get_cal algorithm (which is described in Section 5.3.2).

5.3.1.1 Input Arguments

The variables that follow provide data required by the

get_cal algorithm, and therefore, comprise its input arguments as passed by calibrate_l1a:

• cal_path (character): the directory path and filename for the sensor calibration table file;

• syear (2-byte integer): the year (4 digits) of the

scene start time;

• sday (2-byte integer): the day of the year for the

scene start time;

• eday (2-byte integer): the day of the year for the

scene end time;

• msec (4-byte real): the milliseconds of the day for

the scan start time;

• dtype (character): the data type flag (i.e., GAC or

LAC); and

• tdi (2-byte integer, array size 8): the time TDI

values (0–255) for all eight bands, where the values

represent the indices used to obtain the corresponding detector combinations from a LUT.

5.3.1.2 Output Arguments

The following variables are returned by get_cal to

calibrate_l1a.

 cal_year (2-byte integer): the year (4 digits) of

entry, i.e., update, to the calibration table;

 cal_day (2-byte integer): the day of the year of

entry, i.e., update, to the calibration table;

 temps (4-byte real, array size 256 × 8): the temperature correction coefficients for calibration of the

eight bands, read from the sensor calibration table

file;

 scan_mod (4-byte real, array size 2 × 1285): the

scan modulation correction factors,which are read

from the sensor calibration table file (the array dimensions represent even/odd band number × pixels)—if dtype = ‘GAC’, then only the first 248 values of the pixels dimension are used;

 mirror (4-byte real, array size 2 × 8): the mirror

side 0 and mirror side 1 correction factors, read from

the sensor calibration file;

29
SeaWiFS Algorithms, Part 1

 time_factor (4-byte real, array size 8): the timedependent correction factors for all bands; read from

the sensor calibration table file;

 counts (4-byte real, array size 8 × 4 × 5): the digital counts (zero-offsets corrected) corresponding to

each calibration knee for all gains and bands (dimensions = bands × gains × knees); and

 rads (4-byte real, array size 8 × 4 × 5): the radiances corresponding to each calibration knee for

all gains and bands (dimensions = bands × gains ×

knees).

5.3.1.3 Internal Variables

The following variables are used for processing within

get_cal.

dday (2-byte integer): the scan line day of the year;

dyear (2-byte integer): the scan line year (4 digits);

band (2-byte integer): the index for looping through

bands;

det (2-byte integer): the index for looping through

detectors;

dets (2-byte integer, array size 4): an array for storing the combination of detector indices for a given

TDI;

TDI_list (2-byte integer, array size 256 × 4): TDI

values (the array dimensions is computed from the

number of detector combinations times the number

of detectors);

gain (2-byte integer): the index for looping through

the gains;

scnts (2-byte integer, array size 4): the saturation

counts for the four-detector combination;

offs (2-byte integer, array size 8×4): the zerooffset counts (dimensions are bands × detectors),

read from the sensor calibration table file;

srads (4-byte real, array size 4): the saturation

radiances for the four-detector combination.

slopes (4-byte real, array size 8×4×4): radianceto-count slopes (the array dimension is computed

from the number of bands times the number of gains

times the number of detectors), read from the sensor

calibration table file;

loc_slopes (4-byte real, array size 4): a temporary storage array of the four-detector combination

slopes;

oindex (2-byte integer, array size 4): the indices of

the four-detector combination arranged in ascending order of saturation radiances; and

pixel (2-byte integer): the index for looping

through an array that is the length of a scan line.

5.3.2 Algorithm

This section presents the get_cal algorithm using pseudocode and associated narrative comments.

5.3.2.1 Read Sensor Calibration Table File

First, get_cal determines the day and year of the scan

line. This logic is needed to test and account for the possibility that the scene crosses a day boundary [2400 GMT],

and the possibility that it crosses a year boundary, i.e., the

end of a year.

dday = sday

dyear = syear

if sday <> eday and msec < 43.2E6 then

dday = eday

if dday = 1, dyear = syear + 1

end if

Then the sensor calibration table file is read. The routine

will get data for the most recent entry in the table whose

time range includes the satellite data date and time.

open file cal_path

read scan_mod, temps, and TDI_list

for the most recent entry whose time range includes dyear,

dday, and msec,

read cal_year, cal_day, mirror, time_factor, slopes,

and offs

close file cal_path

5.3.2.2 Set UpReturn Arrays

The highest looping structure cycles through the eight

bands.

do for band = 0 to 7

For each band, the detector combination is determined

from the TDI value.

do for det = 0 to 3

dets(det) = TDI_list(tdi(band),det) - 1

end do

The routine then cycles through each of the four commandable gains.

do gain = 0 to 3

The saturation counts and corresponding radiances for each

detector are determined.

do for det = 0 to 3

scnts(det) = 1023 - offs(band,dets(det))

srads(det) = scnts(det)*slopes(band,gain,dets(det))

loc_slopes(det) = slopes(band,gain,dets(det))

end do

The arrays are sorted according to saturation radiances.

sort the detectors by saturation radiances

place ordered indices of saturation radiances

into oindex

The radiances of the knees (end points and three break

points) of the gain response function are set in ascending

order.

30
C. McClain, K. Arrigo, W. Esaias, M. Darzi, F. Patt, R. Evans, J. Brown, C. Brown, R. Barnes, L. Kumar

rads(band,gain,0) = 0.0

rads(band,gain,1) = srads(oindex(1))

rads(band,gain,2) = srads(oindex(2))

rads(band,gain,3) = srads(oindex(3))

rads(band,gain,4) = srads(oindex(4))

The counts of the knees of the gain response function are

set in ascending order.

counts(band,gain,0) = 0

counts(band,gain,1) = (scnts(oindex(0)) +

srads(oindex(0))/loc_slopes(oindex(1)) +

srads(oindex(0))/loc_slopes(oindex(2)) +

srads(oindex(0))/loc_slopes(oindex(3))) / 4.0

counts(band,gain,2) = (scnts(oindex(0)) +

scnts(oindex(1)) +

srads(oindex(1))/loc_slopes(oindex(2)) +

srads(oindex(1))/loc_slopes(oindex(3))) / 4.0

counts(band,gain,3) = (scnts(oindex(0)) +

scnts(oindex(1)) +

scnts(oindex(2)) +

srads(oindex(2))/loc_slopes(oindex(3))) / 4.0

counts(band,gain,4) = (scnts(oindex(0)) +

scnts(oindex(1)) +

scnts(oindex(2)) +

scnts(oindex(3))) / 4.0

end do

end do

Finally, for GAC data, the scan-modulation correction

factor array is subsampled accordingly. These factors are

stored in the sensor calibration table for an entire LAC

scan line.

if dtype = GAC

do pixel = 0 to 247

scan_mod(1,pixel) = scan_mod(1,146+4*pixel)

scan_mod(2,pixel) = scan_mod(2,146+4*pixel)

end do

end if

return

5.4 SENSOR CALIBRATION TABLE

The sensor calibration table is comprised of a set of

parameters which are required for applying the sensor calibration to raw, i.e., level-1a, data. The table is stored as

a Hierarchical Data Format (HDF) file that is available as

a SeaWiFS product. For operational use, the file is read

by the get_cal routine. Figure 11 presents a schematic

view of the data organization in the file. (See the sensor calibration table product specifications in Darzi et al.

(1995) for the complete details of the HDF file structure

of the file and a complete listing of the metadata and data

parameters.)

The sensor calibration table includes parameters that

will not be changed, as well as parameters which may be

periodically updated. Updates are performed by the SeaWiFS Project and result in the appending of data to the

file’s contents—no data are deleted. Whenever the table

is updated, a new version of the file is made available as a

SeaWiFS product. Results of vicarious calibration studies

may indicate if updates are needed to improve previous

calibration parameter values, or to account for changes in

sensor characteristics.

5.4.1 Parameters Containing Constants

The values of the following arrays will not be modified.

Although they are constant and could have been supplied

by the read routine itself, they are included in the sensor

calibration table file for completeness and convenience:

TDI_list (2-byte integer, array size 256 × 4): TDI

values (the array dimensions = number of detector

combinations × detectors);

temps (4-byte real, array size 256 × 8): the temperature correction coefficients (the array dimensions

represent the digitized temperature × bands); and

scan_mod (4-byte real, array size 2 × 1,285): the

scan modulation correction factors (the array dimensions = the even or odd band number × pixels).

5.4.2 Updatable Parameters

When the sensor calibration is updated, a new entry is

appended to the end of each of the updatable parameters.

An entry, therefore, consists of the values entered for all of

the following parameters as part of an update. Each entry

is associated with a time range specified as part of that entry. A time range defines a period of time corresponding to

SeaWiFS data for which that entry’s calibration parameter

values apply.

A newly-entered time period will supersede part, or all,

of one or more previously entered time periods. Only one

period at a time is allowed to have an open end limit, to

indicate that the period includes the most recent satellite

data. If a new entry is made with an open-ended period,

any such previous entry is modified such that the end of

its period is set to just prior the start of the newly-entered

period.

5.4.2.1 Parameters to Identify Entries

The get_cal routine will always obtain data from the

most recent entry whose time period, defined by Start

Date/Time and End Date/Time, includes the scan line’s

date and time. Note that the parameters described in this

subsection are conceptual, and are not the actual names of

the HDF data objects in the sensor calibration table [see

the sensor calibration table product specifications in Darzi

et al. (1995) for complete details].

• Entry Date: contains the year and the day of the

year that the entry is made;

• Start Date/Time: contains the year, the day of

the year, and the milliseconds of the day for the

start of the time period for which the corresponding

calibration entry applies; and

• End Date/Time: contains the year, the day of the

year, and the milliseconds of the day for the end of

the time period for which the corresponding calibration entry applies; if the values in End Date/Time

are equal to 0, this indicates that no end to the time

31
SeaWiFS Algorithms, Part 1

Fig. 11. Schematic view of the sensor calibration table, showing arrays of constant parameters (curved-corner

rectangles), and updatable parameters in a tabular format.

period of the entry is specified, and that the entry

applies up to the most recent satellite data.

5.4.2.2 Parameters Entered for Each Band

Note that the parameters described in this subsection

are also conceptual (as in Section 5.4.2.1), and are not

the actual names of the HDF data objects in the sensor

calibration table [see the sensor calibration table product

specifications in Darzi et al. (1995) for complete details].

 Slopes (4-byte real): the radiance-to-count slopes.

Each band has a set of 16 values for each gain and

detector combination (4×4), which are read into the

array slopes by get_cal;

 Offsets (2-byte integer): the zero-offset counts.

Each band has a set of 4 values for each detector,

which are read into the array offs by get_cal;

 Time Factor (4-byte real): the time-dependent correction factor. Each band has one value, which is

read into the array time_factor by get_cal; and

 Mirror Factors (4-byte real): the mirror correction factors. Each band has a set of 2 values for each

mirror side which are read into the array mirror by

get_cal.

32
C. McClain, K. Arrigo, W. Esaias, M. Darzi, F. Patt, R. Evans, J. Brown, C. Brown, R. Barnes, L. Kumar

Appendix A

The following tables are taken from Barnes et al. (1994b). Table A-1 corresponds to Table 13of the original document, and

Table A-2 corresponds to Table 14 of the same document.

Table A-1. Band edges (half maximum wavelengths) and extended band edges (1% wavelengths) for SeaWiFS bands

1–8. The center wavelength is calculated from the upper and lower band edges. Results are given for three light sources:

spectrally flat, 5,900 K blackbody, and 2,850 K blackbody. Results are also given for the interference filter only, and for

the system level measurement using the monochromator as a light source.

Nominal Lower Extended Lower Center Upper Upper Extended

Band Band Edges Band Edge Band Edge Wavelength Band Edge Band Edge Source

[nm] [nm] [nm] [nm] [nm] [nm]

1 402–422 394.9 403.1 413.2 423.3 433.4 Spectrally Flat

395.1 403.3 413.3 423.4 433.6 5,900 K

395.9 404.5 414.1 423.7 434.8 2,850 K

393.6 402.4 412.6 422.7 432.3 Filter Only†

393.8 402.3 413.0 423.7 433.8 System Level§

2 433–453 424.0 434.1 443.9 453.7 463.7 Spectrally Flat

424.1 434.2 444.0 453.8 463.7 5,900 K

424.8 435.1 444.6 454.1 464.3 2,850 K

423.3 433.5 443.5 453.5 464.6 Filter Only†

422.3 433.6 444.1 454.6 463.8 System Level§

3480–500 470.7 480.8 491.1 501.4 511.8 Spectrally Flat

470.7 480.8 491.1 501.4 511.8 5,900 K

471.3481.5 491.6 501.6 512.32,850 K

470.1 480.5 490.8 501.2 511.3Filter Only†

468.1 479.1 490.1 501.1 511.7 System Level§

4 500–520 488.1 498.9 510.1 521.3530.7 Spectrally Flat

488.1 498.9 510.1 521.2 530.7 5,900 K

488.9 499.4 510.5 521.5 531.1 2,850 K

487.8 498.7 509.9 521.0 532.9 Filter Only†

487.2 498.6 510.3522.0 530.9 System Level§

5 545–565 536.4 545.5 554.6 563.8 577.3 Spectrally Flat

536.3 545.4 554.6 563.8 577.2 5,900 K

536.9 545.8 554.9 563.9 577.9 2,850 K

536.6 545.5 554.6 563.8 577.1 Filter Only†

535.3 544.6 554.2 563.9 577.0 System Level§

6 660–680 646.8 658.3668.2 678.2 692.7 Spectrally Flat

646.7 658.3668.2 678.1 692.5 5,900 K

646.9 658.5 668.4 678.3692.9 2,850 K

646.8 658.4 668.3678.2 692.8 Filter Only†

646.2 658.8 668.8 678.8 692.2 System Level§

7 745–785 728.0 744.7 764.9 785.0 814.5 Spectrally Flat

727.6 744.6 764.6 784.6 812.9 5,900 K

728.4 745.1 765.1 785.1 815.6 2,850 K

725.1 744.3765.0 785.7 814.2 Filter Only†

‡ 743.3 763.8 784.2 ‡ System Level§

8 845–885 826.7 845.7 866.4 887.0 908.2 Spectrally Flat

826.4 845.5 866.1 886.7 907.5 5,900 K

826.8 845.7 866.5 887.2 908.4 2,850 K

826.5 845.6 866.2 886.9 908.0 Filter Only†

‡ 845.6 866.4 887.2 ‡ System Level§

† Calculated from measurements of the narrow band interference filter only.

§ Calculated from system level measurements using a monochromator as the light source.

‡ Outside of the range of the system level measurements.

33
SeaWiFS Algorithms, Part 1

Table A-2. Calculated out-of-band responses for the eight SeaWiFS bands. The instrument responses are given as

the output of the photodiode in picoamperes. The 5,900 K radiances in the calculations are normalized to the expected

saturation radiance for each band at the nominal center wavelength for each band. The upper and lower extended band

edges also come from Barnes et al. (1994b) in Tables 7 and 8. These results are calculated over the wavelength range

from 380–1,150 nm.

Lower Lower In-Band Upper Upper Out-of-Band

Band Out-of-Band Extended Response Extended Out-of-Band Response

Response [pA] Band Edge [nm] [pA] Band Edge [nm] Response [pA] [%]

1 3.38 395.2 2,175.35 433.6 11.77 0.70

2 9.59 424.1 3,418.80 463.7 1.56 0.33

3 6.48 470.7 4,301.14 511.7 28.08 0.80

4 17.32 488.1 4,586.23 530.7 8.96 0.58

5 39.14 536.6 3,631.84 577.2 46.14 2.35

6 12.66 646.7 2,071.19 692.2 7.84 0.99

7 10.17 727.32,818.97 813.4 29.58 1.41

8 66.36 826.4 2,191.97 907.5 15.43 3.73

Glossary

ASCII American Standard Code for Information Interchange

CVT Calibration and Validation Team

CZCS Coastal Zone Color Scanner

DAAC Distributed Active Archive Center

FPA Focal Plane Assembly

GAC Global Area Coverage

GMT Greenwich Mean Time

GSFC Goddard Space Flight Center

HDF Hierarchical Data Format

IR Infrared

LAC Local Area Coverage

LUT Look-Up Table

NASA National Aeronautics and Space Administration

NIMBUS Not an acronym, but representing a series of NASA

experimental weather satellites carrying a wide variety of atmosphere, ice, and ocean sensors.

OCDM Ocean Color Data Mission

PST Postage Stamp (pixel size designation)

QC Quality Control

SBRC Santa Barbara Research Center

SDPS SeaWiFS Data Processing System

S/C Spacecraft

SEAPAK Not an acronym, but an image display and analysis

package developed at GSFC.

SeaWiFS Sea-viewing Wide Field-of-view Sensor

SWG Science Working Group

TDI Time Delay and Integration

TIROS Television and Infrared Observation Satellite

TOMS Total Ozone Mapping Spectrometer

TOVS TIROS Operational Vertical Sounder

Symbols

bb Backscattering coefficient.

B Variable in the expression for limiting reflectance

(Rlim), defined as 0.33b/Kd.

C Chlorophyll concentration.

F0(λ) Extraterrestrial solar irradiance.

Kd Diffuse attenuation coefficient for downwelling irradiance.

K(440) Diffuse attenuation coefficient at 440 nm.

La Aerosol radiance.

Lg Glint radiance.

Li Incident light.

Lt(λ) Total radiance.

LW (λ) Water-leaving radiance.

LW N (λ) Normalized water-leaving radiance.

n The index of refraction.

PW Probability of seeing sun glint in the spacecraft direction.

Q The ratio of upwelling irradiance to radiance, which

varies with the angular distribution of the upwelling

light field, and is π for an isotropic distribution.

Rlim Limiting reflectance for defining Case-1 water.

Rrs Remote sensing reflectance.

t(750, θ) Diffuse transmittance between the ocean surface and

the sensor at 750 nm.

T0(λ, θ0) Total downward transmittance of irradiance.

W Wind speed.

α750 Albedo at 750 nm.

 Atmospheric correction parameter.

θ Spacecraft zenith angle.

θ0 Solar zenith angle.

λ Wavelength.

34
C. McClain, K. Arrigo, W. Esaias, M. Darzi, F. Patt, R. Evans, J. Brown, C. Brown, R. Barnes, L. Kumar

ρ Fresnel reflectivity.

τa Aerosol radiance.

τox Oxygen optical thickness.

τoz Ozone optical thickness.

τr Rayleigh optical thickness.

φ Azimuth angle at the line-of-sight of a spacecraft.

φ0 Azimuth angle of direct sunlight.

References

Ackleson S.G., and P.M. Holligan, 1989: AVHRR observations

of a Gulf of Maine coccolithophorid bloom. Photogramm.

Eng. Remote Sens., 55, 473–474.

Aiken, J., G.F. Moore, and P.M. Holligan, 1992: Remote sensing of oceanic biology in relation to global climate change.

J. Phycol., 28, 579–590.

Allen, C.W., 1973: Astrophysical Quantities, 3rd Edition. Athalone Press, London, 310 pp.

Arrigo, K.R., and C.R. McClain, 1995: “Cloud and ice detection at high latitudes for processing of CZCS imagery,”

In: McClain, C.R., W.E. Esaias, M. Darzi, F.S. Patt,

R.H. Evans, J.W. Brown, K.R. Arrigo, C.W. Brown, R.A.

Barnes, and L. Kumar, 1995: Case Studies for SeaWiFS

Calibration and Validation, Part 4. NASA Tech. Memo.

104566, Vol. 28, S.B. Hooker, E.R. Firestone, and J.G.

Acker, Eds., NASA Goddard Space Flight Center, 8–12.

Balch, W.M., P.M. Holligan, S.G. Ackleson, and K.J. Voss,

1991: Biological and optical properties of mesoscale coccolithophore blooms in the Gulf of Maine. Limnol. Oceanogr.,

36, 629–643.

Barnes, R.A., W.L. Barnes, W.E. Esaias, and C.L. McClain,

1994a: Prelaunch Acceptance Report for the SeaWiFS Radiometer. NASA Tech. Memo. 104566, Vol. 22, S.B. Hooker, E.R. Firestone, and J.G. Acker, Eds., NASA Goddard

Space Flight Center, Greenbelt, Maryland, 32 pp.

, A.W. Holmes, W.L. Barnes, W.E. Esaias, and C.R. McClain, 1994b: SeaWiFS Prelaunch Radiometric Calibration and Spectral Characterization. NASA Tech. Memo.

104566, Vol. 23, S.B. Hooker, E.R. Firestone, and J.G.

Acker, Eds., NASA Goddard Space Flight Center, Greenbelt, Maryland, 55 pp.

Bricaud, A., and A. Morel, 1987: Atmospheric corrections and

interpretation of marine radiances in CZCS imagery: Use

of a reflectance model. Oceanol. Acta, 7, 33–50.

Brown, C.W., 1995: “Classification of coccolithophore blooms

in ocean color imagery.” In: McClain, C.R., W.E. Esaias, M. Darzi, F.S. Patt, R.H. Evans, J.W. Brown, K.R.

Arrigo, C.W. Brown, R.A. Barnes, and L. Kumar, 1995:

Case Studies for SeaWiFS Calibration and Validation, Part

4. NASA Tech. Memo. 104566, Vol. 28, S.B. Hooker, E.R.

Firestone, and J.G. Acker, Eds., NASA Goddard Space

Flight Center, Greenbelt, Maryland, 13–19.

, and J.A. Yoder, 1994a: Coccolithophorid blooms in the

global ocean. J. Geophys. Res., 99, 7,467–7,482.

, and , 1994b: Distribution pattern of coccolithophorid

blooms in the western North Atlantic. Cont. Shelf Res.,

14, 175–198.

Carder, K.L., W.W. Gregg, D.K. Costello, K. Haddad, and

J.M. Prospero, 1991: Determination of Saharan dust radiance and chlorophyll from CZCS imagery. J. Geophys.

Res., 96, 5,369–5,378.

Cox, C., and W. Munk, 1954: Statistics of the sea surface derived from sun glitter. J. Mar. Res., 13, 198–277.

Darzi, M., 1992: Cloud Screening for Polar Orbiting Visible

and Infrared (IR) Satellite Sensors. NASA Tech. Memo.

104566, Vol. 7, S.B. Hooker and E.R. Firestone, Eds.,

NASA Goddard Space Flight Center, Greenbelt, Maryland, 7 pp.

Darzi, M., F.S. Patt, J.K. Firestone, B. Schieber, L. Kumar,

and D. Ilg, 1995: SeaWiFS Operational Archive Product

Specifications. [World Wide Web page.] From URL: http:

//seawifs.gsfc.nasa.gov/SEAWIFS/SOFTWARE/SOFTWARE

.html see “SeaWiFS Product Specifications (postscript)”.

NASA Goddard Space Flight Center, Greenbelt, Maryland.

Duntley, S.Q., R.W. Austin, W.H. Wilson, C.F. Edgerton, and

M.E. Moran, 1974: Ocean color analysis. Visibility Laboratory of the Scripps Institution of Oceanography Report,

SIO Ref. 74-10, 70 pp.

Eckstein, B.A., and J.J. Simpson, 1991: Cloud screening Coastal

Zone Color Scanner images using channel 5. Int. J. Remote

Sens., 12, 2,359–2,377.

Evans, R.H., and H.R. Gordon, 1994: CZCS “system calibration”: A retrospective examination. J. Geophys. Res., 99,

7,293–7,307.

Feldman, G., N. Kuring, C. Ng, W. Esaias, C. McClain, J. Elrod, N. Maynard, D. Endres, R. Evans, J. Brown, S. Walsh,

M. Carle, and G. Podesta, 1989: Ocean color: Availability

of the global data set. Eos, Trans. AGU, 70, 634.

Firestone, E.R., and S.B. Hooker, 1995: SeaWiFS Technical

Report Series Cumulative Index: Volumes 1–17, NASA

Tech. Memo. 104566, Vol. 18, S.B. Hooker and E.R. Firestone Eds., NASA Goddard Space Flight Center, Greenbelt, Maryland, 16–18.

Gallaudet, T.C., and J.J. Simpson, 1991: Automated cloud

screening of AVHRR imagery using split-and-merge clustering. Remote Sens. Environ., 38, 77–121.

Gordon, H.R., and D.K. Clark, 1981: Clear water radiances

for atmospheric correction of Coastal Zone Color Scanner

imagery. Appl. Opt., 20, 4,175–4,180.

, and A.Y. Morel, 1983: Remote assessment of ocean color

for interpretation of satellite visible imagery: review. Lecture Notes on Coastal and Estuarine Studies, Vol. 4,

Springer-Verlag, 114 pp.

, 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.

35
SeaWiFS Algorithms, Part 1

, J.W. Brown, and R.H. Evans, 1988a: Exact Rayleigh

scattering calculations for use with the Nimbus-7 Coastal

Zone Color Scanner. Appl. Optics, 27, 862–871.

, O.B. Brown, R.H. Evans, J.W. Brown, R.C. Smith, K.S.

Baker, and D.K. Clark, 1988b: A semianalytic radiance

model of ocean color. J. Geophys. Res., 93, 10,909–

10,924.

, and M. Wang, 1994: Retrieval of water-leaving radiance and aerosol optical thickness over the oceans with

SeaWiFS: a preliminary algorithm. Appl. Opt., 33, 443–

452.

Gregg, W.W., F.C. Chen, A.L. Mezaache, J.D. Chen, and J.A.

Whiting, 1993: The Simulated SeaWiFS Data Set, Version

1. NASA Tech. Memo. 104566, Vol. 9, S.B. Hooker, E.R.

Firestone, and A.W. Indest, Eds., NASA Goddard Space

Flight Center, Greenbelt, Maryland, 17 pp.

Holligan, P.M., M. Viollier, D.S. Harbour, P. Camus, and M.

Champagne-Philippe, 1983: Satellite and ship studies of

coccolithophore production along a continental shelf edge.

Nature, 304, 339–342.

, and W.M. Balch, 1991: From the ocean to cells: coccolithophore optics and biogeochemistry. Particle Analysis

in Oceanography, S. Demers, Ed., Springer-Verlag, Berlin,

301–324.

, E. Fernandez, J. Aiken, W.M. Balch, P. Boyd, P.H. Burkill, M. Finch, S.B. Groom, G. Malin, K. Muller, D.A.

Purdie, C. Robinson, C.C. Trees, S.M. Turner, and P. van

der Wal, 1993: A biogeochemical study of the coccolithophore, Emiliania huxleyi, in the North Atlantic. Global

Biogeochem. Cycles, 7, 879–900.

Hooker, S.B., W.E. Esaias, G.C. Feldman, W.W. Gregg, and

C.R. McClain, 1992: An Overview of SeaWiFS and Ocean

Color, NASA Tech. Memo. 104566, Vol. 1, S.B. Hooker

and E.R. Firestone, Eds., NASA Goddard Space Flight

Center, Greenbelt, Maryland, 25 pp., plus color plates.

Martin, D.L., 1992: Minimizing Systematic Errors in Phytoplankton Pigment Concentration Derived from Satellite

Ocean Color Measurements. Ph.D. dissertation, University of Washington, Seattle, Washington, 121 pp.

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

D. Endres, 1991: SEAPAK User’s Guide, Version 2.0, Vol.

II—Descriptions of Programs. NASA Tech. Memo. 100728,

NASA Goddard Space Flight Center, Greenbelt, Maryland, 158 pp.

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

Hooker, B.G. Mitchell, and R. Barnes, 1992: SeaWiFS Calibration and Validation Plan. NASA Tech. Memo. 104566,

Vol. 3, S.B. Hooker and E.R. Firestone, Eds., NASA Goddard Space Flight Center, Greenbelt, Maryland, 41 pp.

, G. Feldman, and W. Esaias, 1993: Oceanic biological

productivity. Atlas of Ssatellite Observations Related to

Global Change, R.J. Gurney, J.L. Foster, and C.L. Parkinson, Eds., Cambridge University Press, 251–263.

, R.S. Fraser, and E-n. Yeh, 1994: “SeaWiFS Pressure

and Oxygen Absorption Study,” In: McClain, C.R., J.C.

Comiso, R.S. Fraser, J.K. Firestone, B.D. Schieber, En. Yeh, K.R. Arrigo, C.W. Sullivan, 1994: Case Studies

for SeaWiFS Calibration and Validation, Part 1. NASA

Tech. Memo. 104566, Vol. 13, S.B. Hooker and E.R. Firestone, Eds., NASA Goddard Space Flight Center, Greenbelt, Maryland, 15–20.

, and E-n. Yeh, 1994a: “Pixel-by-pixel pressure and ozone

correction study,” In: McClain, C.R., J.C. Comiso, R.S.

Fraser, J.K. Firestone, B.D. Schieber, E-n. Yeh, K.R. Arrigo, C.W. Sullivan, 1994: Case Studies for SeaWiFS Calibration and Validation, Part 1. NASA Tech. Memo.

104566, Vol. 13, S.B. Hooker and E.R. Firestone, Eds.,

NASA Goddard Space Flight Center, Greenbelt, Maryland, 21–26.

, and , 1994b: “Sun glint flag sensitivity study,” In:

McClain, C.R., J.C. Comiso, R.S. Fraser, J.K. Firestone,

B.D. Schieber, E-n. Yeh, K.R. Arrigo, C.W. Sullivan, 1994:

Case Studies for SeaWiFS Calibration and Validation, Part

1. NASA Tech. Memo. 104566, Vol. 13, S.B. Hooker and

E.R. Firestone, Eds., NASA Goddard Space Flight Center,

Greenbelt, Maryland, 49–50.

Morel, A., 1988: Optical modeling of the upper ocean in relation to its biogenous matter content (Case I waters). J.

Geophys. Res., 93, 10,749–10,768.

Robbins, L.L., and P.L. Blackwelder, 1992: Biochemical and ultrastructural evidence for the origin of whitings: A biologically induced calcium carbonate precipitation mechanism.

Geology, 20, 464–468.

Schowengerdt, R.A., 1983: Techniques for Image Processing

and Classification in Remote Sensing. Academic Press,

249 pp.

Subramaniam, A., and E.J. Carpenter, 1994: An empirically

derived protocol for the detection of blooms of the marine

cyanobacterium Trichodesmium using CZCS imagery. Int.

J. Remote Sens., 15, 1,559–1,569.

Shinn, J.A., R.P. Steinen, B.H. Lidz, and P.K. Swart, 1989:

Whitings, a sedimentologic dilemma. J. Sed. Petrol., 59,

147–161.

Toratani, M., and H. Fukushima, 1993: Atmospheric correction

scheme for ocean color remote sensing in consideration to

Asian dust aerosol. IGARSS ’93, Vol. 4, IEEE, New York,

1,937–1,940.

Viollier, M., D. Tanre ́e, and P.Y. Deschamps, 1980: An algorithm for remote sensing of water color from space. Bound.-

Layer Meteor., 18, 247–267.

Warneck, P., 1988: Chemistry of the Natural Atmosphere. Academic Press, 757 pp.

The SeaWiFS Technical Report Series

Vol. 1

Hooker, S.B., W.E. Esaias, G.C. Feldman, W.W. Gregg, and

C.R. McClain, 1992: An Overview of SeaWiFS and Ocean

Color. NASA Tech. Memo. 104566, Vol. 1, S.B. Hooker

and E.R. Firestone, Eds., NASA Goddard Space Flight

Center, Greenbelt, Maryland, 24 pp., plus color plates.

36
C. McClain, K. Arrigo, W. Esaias, M. Darzi, F. Patt, R. Evans, J. Brown, C. Brown, R. Barnes, L. Kumar

Vol. 2

Gregg, W.W., 1992: Analysis of Orbit Selection for SeaWiFS:

Ascending vs. Descending Node. NASA Tech. Memo.

104566, Vol. 2, S.B. Hooker and E.R. Firestone, Eds.,

NASA Goddard Space Flight Center, Greenbelt, Maryland, 16 pp.

Vol. 3

McClain, C.R., W.E. Esaias, W. Barnes, B. Guenther, D. Endres, S. 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., NASA Goddard Space Flight Center, Greenbelt,

Maryland, 41 pp.

Vol. 4

McClain, C.R., E. Yeh, and G. Fu, 1992: An Analysis of GAC

Sampling Algorithms: A Case Study. NASA Tech. Memo.

104566, Vol. 4, S.B. Hooker and E.R. Firestone, Eds.,

NASA Goddard Space Flight Center, Greenbelt, Maryland, 22 pp., plus color plates.

Vol. 5

Mueller, J.L., and R.W. Austin, 1992: Ocean Optics Protocols

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

5, S.B. Hooker and E.R. Firestone, Eds., NASA Goddard

Space Flight Center, Greenbelt, Maryland, 43pp.

Vol. 6

Firestone, E.R., and S.B. Hooker, 1992: SeaWiFS Technical

Report Series Summary Index: Volumes 1–5. NASA Tech.

Memo. 104566, Vol. 6, S.B. Hooker and E.R. Firestone,

Eds., NASA Goddard Space Flight Center, Greenbelt,

Maryland, 9 pp.

Vol. 7

Darzi, M., 1992: Cloud Screening for Polar Orbiting Visible

and IR Satellite Sensors. NASA Tech. Memo. 104566, Vol.

7, S.B. Hooker and E.R. Firestone, Eds., NASA Goddard

Space Flight Center, Greenbelt, Maryland, 7 pp.

Vol. 8

Hooker, S.B., W.E. Esaias, and L.A. Rexrode, 1993: Proceedings of the First SeaWiFS Science Team Meeting. NASA

Tech. Memo. 104566, Vol. 8, S.B. Hooker and E.R. Firestone, Eds., NASA Goddard Space Flight Center, Greenbelt, Maryland, 61 pp.

Vol. 9

Gregg, W.W., F.C. Chen, A.L. Mezaache, J.D. Chen, J.A.

Whiting, 1993: The Simulated SeaWiFS Data Set, Version 1. NASA Tech. Memo. 104566, Vol. 9, S.B. Hooker

and E.R. Firestone, and A.W. Indest, Eds., NASA Goddard Space Flight Center, Greenbelt, Maryland, 17 pp.

Vol. 10

Woodward, R.H., R.A. Barnes, C.R. McClain, W.E. Esaias,

W.L. Barnes, and A.T. Mecherikunnel, 1993: Modeling

of the SeaWiFS Solar and Lunar Observations. NASA

Tech. Memo. 104566, Vol. 10, S.B. Hooker and E.R. Firestone, Eds., NASA Goddard Space Flight Center, Greenbelt, Maryland, 26 pp.

Vol. 11

Patt, F.S., C.M. Hoisington, W.W. Gregg, and P.L. Coronado,

1993: Analysis of Selected Orbit Propagation Models for

the SeaWiFS Mission. NASA Tech. Memo. 104566, Vol.

11, S.B. Hooker, E.R. Firestone, and A.W. Indest, Eds.,

NASA Goddard Space Flight Center, Greenbelt, Maryland, 16 pp.

Vol. 12

Firestone, E.R., and S.B. Hooker, 1993: SeaWiFS Technical Report Series Summary Index: Volumes 1–11. NASA Tech.

Memo. 104566, Vol. 12, S.B. Hooker and E.R. Firestone,

Eds., NASA Goddard Space Flight Center, Greenbelt,

Maryland, 28 pp.

Vol. 13

McClain, C.R., K.R. Arrigo, J. Comiso, R. Fraser, M. Darzi,

J.K. Firestone, B. Schieber, E-n. Yeh, and C.W. Sullivan, 1994: Case Studies for SeaWiFS Calibration and Validation, Part 1. NASA Tech. Memo. 104566, Vol. 13,

S.B. Hooker and E.R. Firestone, Eds., NASA Goddard

Space Flight Center, Greenbelt, Maryland, 52 pp., plus

color plates.

Vol. 14

Mueller, J.L., 1993: The First SeaWiFS Intercalibration RoundRobin Experiment, SIRREX-1, July 1992. NASA Tech.

Memo. 104566, Vol. 14, S.B. Hooker and E.R. Firestone,

Eds., NASA Goddard Space Flight Center, Greenbelt,

Maryland, 60 pp.

Vol. 15

Gregg, W.W., F.S. Patt, R.H. Woodward, 1994: The Simulated SeaWiFS Data Set, Version 2. NASA Tech. Memo.

104566, Vol. 15, S.B. Hooker and E.R. Firestone, Eds.,

NASA Goddard Space Flight Center, Greenbelt, Maryland, 42 pp., plus color plates.

Vol. 16

Mueller, J.L., B.C. Johnson, C.L. Cromer, J.W. Cooper, J.T.

McLean, S.B. Hooker, and T.L. Westphal, 1994: The Second SeaWiFS Intercalibration Round-Robin Experiment,

SIRREX-2, June 1993. NASA Tech. Memo. 104566, Vol.

16, S.B. Hooker and E.R. Firestone, Eds., NASA Goddard

Space Flight Center, Greenbelt, Maryland, 121 pp.

Vol. 17

Abbott, M.R., O.B. Brown, H.R. Gordon, K.L. Carder, R.E.

Evans, F.E. Muller-Karger, and W.E. Esaias, 1994: Ocean

color in the 21st century: a strategy for a 20-year time

series. NASA Tech. Memo. 104566, Vol. 17, S.B. Hooker

and E.R. Firestone, Eds., NASA Goddard Space Flight

Center, Greenbelt, Maryland, 20 pp.

Vol. 18

Firestone, E.R., and S.B. Hooker, 1995: SeaWiFS Technical Report Series Summary Index: Volumes 1–17. NASA Tech.

Memo. 104566, Vol. 18, S.B. Hooker and E.R. Firestone,

Eds., NASA Goddard Space Flight Center, Greenbelt,

Maryland, 47 pp.

37
SeaWiFS Algorithms, Part 1

Vol. 19

McClain, C.R., R.S. Fraser, J.T. McLean, M. Darzi, J.K. Firestone, F.S. Patt, B.D. Schieber, R.H. Woodward, E-n. Yeh,

S. Mattoo, S.F. Biggar, P.N. Slater, K.J. Thome, A.W.

Holmes, R.A. Barnes, and K.J. Voss, 1994: Case Studies

for SeaWiFS Calibration and Validation, Part 2. NASA

Tech. Memo. 104566, Vol. 19, S.B. Hooker, E.R. Firestone,

and J.G. Acker, Eds., NASA Goddard Space Flight Center,

Greenbelt, Maryland, 69 pp.

Vol. 20

Hooker, S.B., C.R. McClain, J.K. Firestone, T.L. Westphal,

E-n. Yeh, and Y. Ge, 1994: The SeaWiFS Bio-Optical

Archive and Storage System (SeaBASS), Part 1. NASA

Tech. Memo. 104566, Vol. 20, S.B. Hooker and E.R. Firestone, Eds., NASA Goddard Space Flight Center, Greenbelt, Maryland, 40 pp.

Vol. 21

Acker, J.G., 1994: The Heritage of SeaWiFS: A Retrospective on the CZCS NIMBUS Experiment Team (NET) Program. NASA Tech. Memo. 104566, Vol. 21, S.B. Hooker

and E.R. Firestone, Eds., NASA Goddard Space Flight

Center, Greenbelt, Maryland, 43pp.

Vol. 22

Barnes, R.A., W.L. Barnes, W.E. Esaias, and C.R. McClain,

1994: Prelaunch Acceptance Report for the SeaWiFS Radiometer. NASA Tech. Memo. 104566, Vol. 22, S.B. Hooker, E.R. Firestone, and J.G. Acker, Eds., NASA Goddard

Space Flight Center, Greenbelt, Maryland, 32 pp.

Vol. 23

Barnes, R.A., A.W. Holmes, W.L. Barnes, W.E. Esaias, C.R.

McClain, and T. Svitek, 1994: SeaWiFS Prelaunch Radiometric Calibration and Spectral Characterization. NASA

Tech. Memo. 104566, Vol. 23, S.B. Hooker, E.R. Firestone,

and J.G. Acker, Eds., NASA Goddard Space Flight Center,

Greenbelt, Maryland, 55 pp.

Vol. 24

Firestone, E.R., and S.B. Hooker, 1995: SeaWiFS Technical Report Series Summary Index: Volumes 1–23. NASA Tech.

Memo. 104566, Vol. 24, S.B. Hooker and E.R. Firestone,

Eds., NASA Goddard Space Flight Center, Greenbelt,

Maryland, 36 pp.

Vol. 25

Mueller, J.L., and R.W. Austin, 1995: Ocean Optics Protocols

for SeaWiFS Validation, Revision 1. NASA Tech. Memo.

104566, Vol. 25, S.B. Hooker and E.R. Firestone, Eds.,

NASA Goddard Space Flight Center, Greenbelt, Maryland, 67 pp.

Vol. 26

Siegel, D.A., M.C. O’Brien, J.C. Sorensen, D.A. Konnoff, E.A.

Brody, J.L. Mueller, C.O. Davis, W.J. Rhea, and S.B.

Hooker, 1995: Results of the SeaWiFS Data Analysis

Round-Robin (DARR), July 1994. NASA Tech. Memo.

104566, Vol. 26, S.B. Hooker and E.R. Firestone, Eds.,

NASA Goddard Space Flight Center, Greenbelt, Maryland, 58 pp.

Vol. 27

Mueller, J.L., R.S. Fraser, S.F. Biggar, K.J. Thome, P.N. Slater,

A.W. Holmes, R.A. Barnes, C.T. Weir, D.A. Siegel, D.W.

Menzies, A.F. Michaels, and G. Podesta 1995: Case Studies for SeaWiFS Calibration and Validation, Part 3. NASA

Tech. Memo. 104566, Vol. 27, S.B. Hooker, E.R. Firestone,

and J.G. Acker, Eds., NASA Goddard Space Flight Center,

Greenbelt, Maryland, 46 pp.

Vol. 28

McClain, C.R., K.R. Arrigo, W.E. Esaias, M. Darzi, F.S. Patt,

R.H. Evans, J.W. Brown, C.W. Brown, R.A. Barnes, and

L. Kumar, 1995: SeaWiFS Algorithms, Part 1. NASA

Tech. Memo. 104566, Vol. 28, S.B. Hooker, E.R. Firestone,

and J.G. Acker, Eds., NASA Goddard Space Flight Center,

Greenbelt, Maryland, 38 pp., plus color plates.

38
C. McClain, K. Arrigo, W. Esaias, M. Darzi, F. Patt, R. Evans, J. Brown, C. Brown, R. Barnes, L. Kumar

COLOR PLATES
SeaWiFS Algorithms, Part 1

PLATE 1. Scene 1: CZCS pigment image from the western Weddell Sea for 1 March 1986. The image was processed

assuming α750 = 1.6%, and using a bilinearly interpolated surface pressure field.
C. McClain, K. Arrigo, W. Esaias, M. Darzi, F. Patt, R. Evans, J. Brown, C. Brown, R. Barnes, L. Kumar

PLATE 2. Scene 1: CZCS pigment image from the western Weddell Sea for 1 March 1986. The image was processed

assuming α750 = 0.7%, and using bilinearly interpolated surface pressure fields.
SeaWiFS Algorithms, Part 1

PLATE 3. The difference in computed pigments for scene 1 (processed assuming α750 = 0.7%) between the use of

pixel-by-pixel variability in observed surface pressure, and using a spatially constant surface pressure of 1,013.25 mb.
C. McClain, K. Arrigo, W. Esaias, M. Darzi, F. Patt, R. Evans, J. Brown, C. Brown, R. Barnes, L. Kumar

PLATE 4. Scene 2: CZCS pigment image from the North Atlantic for 28 July 1980. The image was processed assuming

α750 = 1.6%, and with a spatially constant surface pressure of 1,013.25 mb.
SeaWiFS Algorithms, Part 1

PLATE 5. Scene 2: CZCS pigment image from the North Atlantic for 28 July 1980. The image was processed assuming

α750 = 0.7%, and with a spatially constant surface pressure of 1,013.25 mb.
C. McClain, K. Arrigo, W. Esaias, M. Darzi, F. Patt, R. Evans, J. Brown, C. Brown, R. Barnes, L. Kumar

PLATE 6. In the top half of this plate, two true-color composites of coccolithophore blooms are shown. The left panel,

a scene from CZCS orbit 8876, shows a bloom south of Iceland. The right panel, from CZCS orbit 3171, shows a bloom

that occurred on the northeastern continental shelf of the US. The coccolithophore blooms are colored milky white. In

the bottom half, the left panel shows the application of a coccolithophore bloom mask to the image shown at top left.

The right panel shows the same mask applied to the image shown at top right. The coccolithophore mask is colored

white and the non-bloom class is colored black.
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Laboratory for Hydrospheric Processes

Goddard Space Flight Center 95B00091

Greenbelt, Maryland 20771

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

 AGENCY REPORT NUMBER

National Aeronautics and Space Administration

Washington, D.C. 20546–0001 TM–104566, Vol. 28

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

13. ABSTRACT (Maximum 200 words)

This document provides five brief reports that address several algorithm investigations sponsored by the Calibration and

Validation Team (CVT) within the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) Project. This volume, therefore, has

been designated as the first in a series of algorithm volumes. Chapter 1 describes the initial suite of masks, used to prevent

further processing of contaminated radiometric data, and flags, which are employed to mark data whose quality (due to a

variety of factors) may be suspect. In addition to providing the mask and flag algorithms, this chapter also describes the

initial strategy for their implementation. Chapter 2 evaluates various strategies for the detection of clouds and ice in high

latitude (polar and sub-polar regions) using Coastal Zone Color Scanner (CZCS) data. Chapter 3 presents an algorithm

designed for detecting and masking coccolithophore blooms in the open ocean. Chapter 4 outlines a proposed scheme for

correcting the out-of-band response when SeaWiFS is in orbit. Chapter 5 gives a detailed description of the algorithm

designed to apply sensor calibration data during the processing of level-1b data.

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Unclassified–Unlimited

Subject Category 48

 -

SeaWiFS, Oceanography, Algorithms,Control Masks, Flags, CZCS Imagery 38

Cloud Detection, Ice Detection, Coccolithophore Blooms, Ocean Color Imagery,

Correction Scheme, Sensor Calibration, Level-2 Processing

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

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

 Unclassified Unclassified Unclassified Unlimited

 REPORT DOCUMENTATION PAGE OMB No. 0704-0188

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

 SeaWiFS Technical Report Series

Volume 28–SeaWiFS Algorithms, Part 1 Code 970.2

 May 1995 Technical Memorandum

6. AUTHOR(S) Charles R. McClain, Kevin R. Arrigo, Wayne E. Esaias, Michael

Darzi, Frederick S. Patt, Robert H. Evans, James W. Brown, Christopher W. Brown,

Robert A. Barnes, and Lakshmi Kumar

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

 Technical Editor: James G. Acker

 Michael Darzi, Frederick S. Patt, and Elaine R. Firestone: General Sciences Corporation,

Laurel, Maryland; Robert H. Evans and James W. Brown: University of Miami, Miami, Florida; Christopher W. Brown:

National Research Council, Washington, DC; Robert A. Barnes: ManTech Environmental Technology, Inc., Wallops Island,

Virginia; and Lakshmi Kumar and James G. Acker: Hughes STX, Lanham, Maryland