NASA/TM-2001-209976

SIMBIOS Project 2000 Annual Report

Giulietta S. Fargion, SAIC General Sciences Corporation, Beltsville, Maryland

Charles R. McClain, Goddard Space Flight Center, Greenbelt, Maryland

National Aeronautics and

Space Administration

Goddard Space Flight Center

Greenbelt, Maryland 20771

SIMBIOS Project Annual Report

ii

Preface

The purpose of this technical report is to provide current documentation of the the Sensor Intercomparison and Merger

for Biological and Interdisciplinary Oceanic Studies (SIMBIOS) Project activities, NASA Research Announcement

(NRA) research status, satellite data processing, data product validation, and field calibration. This documentation is

necessary to ensure that critical information is related to the scientific community and NASA management. This critical

information includes the technical difficulties and challenges of validating and combining ocean color data from an

array of independent satellite systems to form consistent and accurate global bio-optical time series products. This

technical report is not meant as a substitute for scientific literature. Instead, it will provide a ready and responsive

vehicle for the multitude of technical reports issued by an operational project.

The SIMBIOS Science Team Principal Investigators (PIs) original contributions to this report are in chapters four and

above. The purpose of these contributions is to describe the current research status of the SIMBIOS-NRA-96 funded

research. The contributions are published as submitted, with the exception of minor edits to correct obvious

grammatical or clerical errors.
SIMBIOS Project Annual Report

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Table of Contents

1. AN OVERVIEW OF SIMBIOS PROJECT ACTIVITIES AND ACCOMPLISHMENTS DURING FY00 ..................... 1

2. SIMBIOS: SCIENCE TEAM AND CONTRACTS............................................................................................................. 6

3. SIMBIOS PROJECT DATA PROCESSING AND ANALYSIS RESULTS ...................................................................... 8

4. VALIDATION OF SURFACE BIO-OPTICAL PROPERTIES IN THE GULF OF MAINE AS A MEANS FOR

IMPROVING SATELLITE PRIMARY PRODUCTION ESTIMATES ............................................................................... 26

5. OCTS AND SEAWIFS BIO-OPTICAL ALGORITHM AND PRODUCT VALIDATION AND INTERCOMPARISON

IN U.S. COASTAL WATERS ............................................................................................................................................... 34

5. VALIDATION OF OCEAN COLOR SATELLITE DATA PRODUCTS IN UNDER SAMPLED MARINE AREAS .. 42

6.BIO-OPTICAL MEASUREMENTS AT OCEAN BOUNDARIES IN SUPPORT OF SIMBIOS .................................... 51

7. HIGH FREQUENCY, LONG TIME SERIES MEASUREMENTS FROM THE BERMUDA TESTBED MOORING IN

SUPPORT OF SIMBIOS ....................................................................................................................................................... 58

8. REMOTE SENSING OF OCEAN COLOR IN THE ARCTIC: ALGORITHM DEVELOPMENT AND

COMPARATIVE VALIDATION.......................................................................................................................................... 65

9. SATELLITE OCEAN COLOR VALIDATION USING MERCHANT SHIPS ................................................................ 71

10. HIGH ALTITUDE MEASUREMENTS OF RADIANCE AT HIGH SPECTRAL AND SPATIAL RESOLUTION FOR

SIMBIOS SENSOR CALIBRATION,VALIDATION, AND INTERCOMPARISONS....................................................... 80

11. MERGING OCEAN COLOR DATA FROM MULTIPLE MISSIONS.......................................................................... 84

12.BIO-OPTICAL MEASUREMENT AND MODELING OF THE CALIFORNIA CURRENT AND POLAR OCEANS

..................................................................................................................................................91

13 SIMBIOS NORMALIZED WATER-LEAVING RADIANCE CALIBRATION AND VALIDATION: SENSOR

RESPONSE, ATMOSPHERIC CORRECTIONS, STRAY LIGHT AND SUN GLINT ...................................................... 99

14. VALIDATION OF CARBON FLUX & RELATED PRODUCTS FOR SIMBIOS: THE CARIACO CONTINENTAL

MARGIN TIME SERIES AND THE ORINOCO RIVER PLUME .................................................................................... 101

15. THE BERMUDA BIO-OPTICS PROGRAM (BBOP).................................................................................................. 109

16. SPECTRAL DATA ASSIMILATION FOR MERGING SATELLITE OCEAN COLOR IMAGERY ........................ 117

17. SIMBIOS DATA PRODUCT AND ALGORITHM VALIDATION WITH EMPHASIS ON THE

BIOGEOCHEMICAL AND INHERENT OPTICAL PROPERTIES.................................................................................. 125

18. AEROSOL OPTICAL PROPERTIES OVER THE OCEANS: SUMMARY AND INTERPRETATION OF SHADOWBAND RADIOMETER DATA FROM SIX CRUISES....................................................................................................... 132

19. MEASUREMENTS OF AEROSOL, OCEAN AND SKY PROPERTIES AT THE HOT SITE IN THE CENTRAL

PACIFIC............................................................................................................................................................................... 140
SIMBIOS Project Annual Report

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20. ASSESSMENT OF THE CONTRIBUTION OF THE ATMOSPHERE TO UNCERTAINTIES IN NORMALIZED

WATER-LEAVING RADIANCE: A COMBINED MODELING AND DATA ANALYSIS APPROACH...................... 146

21. SOURCES OF VARIABILITY IN CHLOROPHYLL ANALYSIS BY FLUOROMETRY AND BY HIGH

PERFORMANCE LIQUID CHROMATOGRAPHY.......................................................................................................... 149

22. VALIDATION OF THE WATER-LEAVING RADIANCE DATA PRODUCT ......................................................... 157

GLOSSARY......................................................................................................................................................................... 163
SIMBIOS Project Annual Report

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Chapter 1

An Overview of SIMBIOS Project Activities and

Accomplishments During FY00

Charles R. McClain

NASA Goddard Space Flight Center, Greenbelt, Maryland

Giulietta S. Fargion

SAIC General Sciences Corporation, Beltsville, Maryland

In FY00, the SIMBIOS Project brought a number

of activities to fruition and laid the groundwork for

new initiatives in FY01. Of particular importance was

ensuring that the SIMBIOS program was renewed and

a new science team selected in FY00. With SeaWiFS

continuing to perform well, the Terra platform and

MODIS being launched in December 1999, and several

other global missions scheduled for launch (e.g., the

Aqua platform with a second MODIS (2001), the

ENVISAT mission with MERIS (2001), and ADEOSII with GLI and POLDER) the first real opportunties to

merge global data sets can be pursued. NASA HQ did

approve a three-year continuation of the science team

under an NRA and the Project Office. As it is taking

some time for the MODIS team to work through the

initial on-orbit processing issues, merger activities

within the Project Office have been a low priority with

most of the emphasis being on other program

objectives which will provide a broader foundation and

capabilities for working with multiple global data sets

in the future. These activities and accomplishments are

described below.

1. SIMBIOS Science Team Support

a. A science team meeting was held in Annapolis in

late 1999, but no team meeting was held 2000. It

was decided that the second science team meeting

should occur in January 2001 when the new

science team would be officially in place.

b. Under a SIMBIOS NRA released in late 1999, a

new science team was selected. The new team

consists of 19 U.S. and 12 international

investigations with addition international

investigations to be added as post-NRA proposals

are received and accepted. The Project Office

negotiated the revised statements of work and

budgets required for the agreements (contracts,

memoranda of understandings, etc., depending on

whether the organization was a university or a

government agency) with each member of the new

science team. In cases of reselected investigations,

bridging arrangements were executed and contract

close-out procedures were executed for all

contracts of the first science team. Agreements

with the international team members are being

initiated by NASA/HQ.

c. In June, the SIMBIOS Project management

conducted team performance evaluation of all

investigations. Formal evaluations of

investigations funded through contracts are

required by the NASA Procurement Office.

d. The Project Office provided cruise support

(satellite coverage information, near-realtime data

products, etc.) to 52 experiments (not all by

Science Team members).

2. Satellite Data Analyses

a. Reception, processing, distribution of IRS-P3

MOS data continued operationally. The data is

realtime broadcast as captured at Wallops Flight

Facility using a subsystem purchased from a firm

in India. Therefore, the coverage is the western

North Atlantic and eastern continental U.S. and

Canada. When the spacecraft is configured for

MOS data collection (periods of space data

collection using another instrument precludes

MOS operations), the data is captured, networked

to the Project Office, processed, and distributed

on-line via a browse capability.

b. The calibration of the OCTS and POLDER using

MOBY comparisons and the SeaWiFS

atmospheric correction scheme was completed.

The analyses provide consistent data products

from the two instruments using common match-up

and regional average value comparisons.

c. The SIMBIOS science team and Project staff

played a major role in defining the algorithm

improvements incorporated into the SeaWiFS

reprocessing #3 (McClain et al., 2000a,b and

O’Reilly et al., 2000). The SeaWiFS Project held

workshops and algorithm round robins in

preparation for the reprocessing in which science
SIMBIOS Project Annual Report

2

team members and Project staff actively

participated.

d. In October, the Korean Aerospace Research

Institute (KARI) solicited the Project’s assistance

in calibrating the KOMPSAT/OSMI. The Project

is currently working with the KOMPSAT program

to gain better insight into the instrument

characteristics and to obtain coverage over

MOBY.

e. In November, the Project was approached by

NASDA about assisting in a reprocessing of the

entire OCTS GAC data set based on the SeaWiFS

data quality. The Project has accepted this

invitation to collaborate and is working on

logistical matters with NASDA.

3. Sunphotometer Data Acquisition

a. Project supported AERONET coastal and island

CIMEL sites were added at Oahu (Hawaii), the

Azores, Turkey, and Argentina. Another site in

Australia will be instrumented in early 2001.

b. A consistent end-to-end data processing procedure

was developed for the suite of sun photometers

that provide data to the Project. The different

instruments include the Microtops hand-held

instrument, CIMEL, the Brookhaven shadow-band

radiometer, SIMBAD, and PREDE. The analysis

procedures include calibration, derived product

generation, quality control, archival in SeaBASS,

and satellite match up analyses. The procedures

are being documented in a NASA technical

memorandum which will be published in early

2001.

c. The Project has two PREDE systems, one of

which has a shipboard stabilizer. The Project

purchased a second stabilizer so that both systems

could be ship-deployed. The Project has also

worked with the manufacturer on some design

problems that have been encountered.

4. Bio-Optical Data Evaluation And Synthesis

a. The SeaBASS data volume continues to grow

steadily as Science Team members and others

submit new data sets. The sun photometer data

from the coastal CIMEL sites and the shipboard

observations is also being ingested into the

archive. Data quality control tests are being

refined and automated in order to keep up with the

increasing volume of new data. In order to make

SeaBASS more efficient and easier to use, the

archive was redesigned and rebuilt over the past

year.

b. The in situ measurement protocols were updated,

expanded, and published (Fargion and Mueller,

2000). The format of this protocol manual was

changed so that specific topics were included as

chapters with different groups of authors for each

chapter.

c. The SeaWiFS and SIMBIOS Projects have

invested heavily in activities focused on the

improvement of in situ radiometric data.

However, questions regarding the accuracy of the

pigment measurements had not been adequately

addressed until this year. The SeaWiFS field

program under Stan Hooker conducted a limited

pigment round robin between international

laboratories he has been working with. The

SIMBIOS Project also conducted a joint round

robin with the Office of Naval Research that

included a number of U.S. laboratories. The round

robin was conducted by the University of

Maryland/Horn Point Environmental Laboratory.

The results of both round robins have been

finalized and summarized in chapter 22 and will be

followed by a more detailed NASA TM

publication in early 2001. The results of the

SeaWiFS round robin were quite satisfactory, but

the SIMBIOS round robin showed sizable errors in

some pigment analyses. Thus, the SIMBIOS

round robin results support the strategy

implemented under the SIMBIOS NRA of having

one laboratory process all the Science Team’s

pigment data using the latest HPLC technology.

5. Radiometric Data Quality Assurance

a. In order to monitor field instrument performance

between calibrations and reduce the cost of

frequent calibrations, the SeaWiFS Project

commissioned NIST to design a stable field

deployable source. The NIST source, the

SeaWiFS Quality Monitor (SQM; Johnson et al.,

1998), has proven to be extremely reliable through

extensive field testing in programs such as the

Atlantic Meridional Transect (AMT; Hooker and

Aiken, 1998) cruises. Seed funding of commercial

versions of the SQM was provided in 1996 to two

companies by the SIMBIOS Project. This year,

the Project purchased one system and has worked

with the other company in product evaluation

testing. Ultimately, the Project plans to provide

funds to U.S. science team members who collect

bio-optical data to purchase an SQM.

b. The SeaWiFS Project also had NIST design a

portable transfer radiometer, the SeaWiFS

Transfer Radiometer (SXR; Johnson et al., 1998),

to be used in calibration round robins and in

satellite instrument calibrations. The original SXR

proved to very accurate and has been used

extensively by the SeaWiFS Project. The

SIMBIOS Project has assumed the responsibility

of continuing the calibration round robin activity

and has published the results of one experiment

(Riley and Bailey, 1998). The SIMBIOS Project
SIMBIOS Project Annual Report

3

purchased a copy of the SXR for future round

robins, but the instrument has suffered stability

problems. With much testing and interaction with

the manufacturer, the problems have been resolved

and the instrument has been recalibrated at NIST.

This sets the stage for the next round robin

experiment.

6. Outreach

a. Project staff provided technical support for MOS

and OCTS processing in the SeaWiFS Data

Analysis System (SeaDAS). The SIMBIOS and

SeaWiFS Projects also provided system

administration, software maintenance, and

hardware upgrade support to the SeaDAS

program.

b. From September 1999 through December 2000,

the SeaWiFS and SIMBIOS Projects sent

representatives to 28 national and international

conferences and workshops, in addition to several

SeaWiFS and MODIS workshops. In most cases,

presentations were made and the

SeaWiFS/SIMBIOS booth was manned at eight of

the conferences.

With these accomplishments, the SIMBIOS Project is

well positioned to undertake a number of new

initiatives in 2001. These include, but are not limited

to, the following:

• Collaborate with NASDA on the reprocessing of

the OCTS GAC data set,

• Assist the KARI in the calibration and processing

of the KOMPSAT/OSMI data,

• Work with the MOS science team on the use of

MOS for linking the SeaWiFS with the ADEOS-I

OCTS and POLDER time series,

• Assist the SeaDAS group on incorporating

POLDER processing capabilities into SeaDAS,

• Work with the MODIS oceans team and Science

Team members on SeaWiFS-MODIS data merger,

• Continue the development of a diagnostic ocean

color data set, a recommended by members of the

IOCCG,

• Design and execute the next calibration round

robin experiment with the new Science Team, and

• Begin preparations for the ENVISAT/MERIS

(2001) and ADEOS-II/GLI (2002) missions. The

Project hosted a NASDA representative for two

months in late 2000 to assist in the GLI

preparations.

Finally, in September 2000, the SeaWiFS Project

Office, working with other scientists and engineers at

GSFC (which also hosts the SIMBIOS Project),

assumed new responsibilities in assisting NASA HQ to

develop a long-term program for global carbon cycle

research. As a result, the Project Office has been

reorganized somewhat to allow Chuck McClain to

focus on the carbon initiative. Gene Feldman and

Giulietta Fargion assumed additional management

responsibilities for SeaWiFS and SIMBIOS,

respectively, as shown in Figure 1.1. Figure 1.2 and

table 1.1 provides a more detailed organization chart of

the SIMBIOS Project. The SIMBIOS and SeaWiFS

Projects are largely staffed by on-site contractors. The

ocean color support contract under which these

personnel work expired in 2000. Beginning in late

1998, Project civil service staff worked on a new

procurement package for support services that was

released and competed for in early 2000. As a result of

the solicitation and bidding competition, with the

incumbent, SAIC/General Sciences Corporation, was

selected as the prime contractor. The contract is for

five years, that will provide continuity of effort beyond

the renewed SIMBIOS three-year program.

REFERENCES

Fargion, G. S., and J. L. Mueller, 2000: Ocean Optics

for Satellite Ocean Color Sensor Validation,

Revision 2, NASA/TM-2000-209966, NASA

Goddard Space Flight Center, Greenbelt, MD, 184

pp.

Johnson, B. C., P.-S. Shaw, S. B. Hooker, and D.

Lynch, 1998: Radiometric and engineering

performance of the SeaWiFS quality monitor

(SQM): a portable light source for field

radiometers, J. Atmos. Oceanic Technol., 5(4),

1008-1022.

Johnson, B. C., J. B. Fowler, and C. L. Cromer, 1998:

The SeaWiFS Transfer Radiometer (SQM),

NASA/TM-1998-206892, Vol. 1, S. B. Hooker

and E. R. Firestone, eds., NASA Goddard Space

Flight Center, Greenbelt, MD, 58 pp.

Hooker, S. B., and J. Aiken, 1998: Calibration

evaluation and radiometric testing of field

radiometers with the SeaWiFS quality monitor

(SQM), J. Atmos. Oceanic Technol., 5(4), 995-

1007.

McClain, C. R. and others, 2000a: SeaWiFS

Postlaunch Calibration and Validation Analyses,

Part 1, NASA/TM-2000-206892, Vol. 9., S. B.

Hooker and E. R. Firestone (eds.), NASA Goddard

Space Flight Center, Greenbelt, Maryland, 82 pp.

McClain, C. R. and others, 2000b: SeaWiFS

Postlaunch Calibration and Validation Analyses,

Part 2, NASA/TM-2000-206892, Vol. 10., S. B.

Hooker and E. R. Firestone (eds.), NASA Goddard

Space Flight Center, Greenbelt, Maryland, 57 pp.
SIMBIOS Project Annual Report

4

O’Reilly, J. E. and others, 2000: SeaWiFS Postlaunch

Calibration and Validation Analyses, Part 3,

NASA/TM-2000-206892, Vol. 11, S. B. Hooker

and E. R. Firestone (eds.), NASA Goddard Space

Flight Center, Greenbelt, Maryland, 49 pp.

Riley, T., and S. Bailey, 1998: The sixth

SeaWiFS/SIMBIOS intercalibration round robin

experiment (SIRREX-6), August-December 1997,

NASA/TM-1998-206878, NASA Goddard Space

Flight Center, Greenbelt, Maryland, 26 pp.

Figure 1.1 Organization chart of the Office for Global Carbon Studies.

Table 1.1 SIMBIOS Project Personnel

Ewa Ainsworth

Irene Antonenko*

Sean Bailey*

Robert Barnes *

Patty Clow *

Giulietta Fargion

Gene Feldman *

Bryan Franz *

Joel Gales*

Lynne Hoppel *

Kirk Knobelspiesse*

Sung Lee *

Kathy Lingerfelt*

Charles McClain *

Gerhard Meister

Kevin Miller*

Fred Patt *

Christophe Pietras

India Robinson *

Paul Smith *

Judy Stubblefield*

Tamara Tucker *

Jeremy Werdell*

Bill Woodford*

SAIC General Sciences Corporation

SAIC General Sciences Corporation

FutureTech Corporation

SAIC General Sciences Corporation

NASA

SAIC General Sciences Corporation

NASA

SAIC General Sciences Corporation

FutureTech Corporation

NASA

SSAI Corporation

SAIC General Sciences Corporation

NASA

NASA

FutureTech Corporation

FutureTech Corporation

SAIC General Sciences Corporation

SAIC General Sciences Corporation

NASA

SAIC General Sciences Corporation

SAIC General Sciences Corporation

SAIC General Sciences Corporation

SSAI Corporation

FutureTech Corporation

* shared with SeaWiFS Project
5

* The SIMBIOS Project is co-located with the SeaWiFS Project and many of the SIMBIOS 

G. Feldman

Satellite Data Processing

Satellite Data ProcessingSatellite Data AnalysisData Set ManagementB. Franz*J. GalesProcessing System SupportS. Lee *P. Smith *B. Woodford *

G. Fargion

Sensor Engineering 

& Calibration 

SIRREX ProgramAERONET InterfaceC. PietrasG. MeisterK. KnobelspiesseSatellite Sensor Characterization andData CalibrationR. Barnes*Field Instrument PoolS. Bailey*

Technical SupportI. Antonenko*System SupportAdministration SupportResources SupportI. Robinson *J. Stubblefield*T. Tucker*Project ScienceC. McClainSIMBIOS ScienceTeam SupportG. Fargion

SIMBIOS Project Office

G. Fargion

Project Manager

SIMBIOS Project Organization Cha

F. P

Contract Sta

P. Clow * L. Hoppel*K. Lingerfelt*

Procurement/Financial

Support

Figure 1.2 SIMBIOS Project organization chart
SIMBIOS Project Annual Report

6

Chapter 2

SIMBIOS: Science Team and Contracts

Giulietta S. Fargion

SAIC General Sciences Corporation, Beltsville, Maryland

2.1 SCIENCE TEAM

The Science Team is selected through NASA

Research Announcement (NRA). Presently NASA has

had two NRA’s, in 1996 and 1999. NASA HQ

manages the process of team selection, but the Goddard

Space Flight Center (GSFC) NASA Procurement

Office handles the team contracts, work statements

and, if necessary, budget negotiations. The Project

funds numerous US investigators and collaborates with

several international investigators, space agencies (e.g.,

NASDA, CNES, KARI, etc.) and international

organizations (e.g., IOCCG, JRC). US investigators

under contract provide in situ atmospheric and biooptical data sets, and develop algorithms and

methodologies for data merger schemes. NASA GSFC

Procurement requires formal evaluations for all

contracts at the end of each contract year. These

evaluations are to go into a database and are shared

with the PI’s institution or upper management.

The locations of specific SIMBIOS team

investigations (i.e., NRA-96 and NRA-99) are shown

in Figures 2.1 and 2.2. The international ocean color

community response for the NRA-99 was

overwhelming, with a total of 75 PI’s attempting to

collaborate with the Project group in twelve proposals.

The twelve international proposals cover topics ranging

from protocols, calibration-validation activities,

atmospheric-biological algorithms, and data merging.

The SIMBIOS Science Team (NRA-96) meetings

were held in August 1997 at Solomons Island

(Maryland), in September 1998 at La Jolla (California)

and in September 1999 at Annapolis (Maryland).

Meanwhile, the SIMBIOS Science Team’s (NRA-99)

first meeting will be held in January 2001 at GSFC in

Greenbelt, Maryland.

In general, all Science Team members, US and

international, were in attendance or were represented

by one of their staff. Also, the IOCCG and MODIS

Teams were invited, and most attended or sent

representatives. During each year the Project Office

has fostered international collaborations by hosting

visiting scientists at GSFC. These visits lasted from

one week to several months. For international

participants, the Project provided partial or as-needed

travel support to team meetings or to collaborate with

the project on specific topics such as calibration, and

product evaluations. Chapters 4 to 23 contain the

individual PI’s contributions of the first SIMBIOS

team (i.e., NRA-96) and describe the funded research

topics, field studies activities, and results of concluded

research. These chapters are reproduced as submitted

with minimal editing by thr Project Office.

2.2 CONTRACT OVERVIEW

The third-year SIMBIOS NRA-96 contracts ended

in the July-September 2000 time frame. Close-out

procedures were executed for all contracts of the first

science team (NRA-96). The Project granted no-cost

extensions to all the PIs that requested it.

Due to NASA HQ funding changes for the new

SIMBIOS Science Team (NRA-99), the Project Office

had to renegotiate all the statements of work and

prepare new budgets for each member of the new

science team. The renegotiated agreements where then

formalized in contracts or memoranda of

understanding, depending on whether the organization

was a university or a government agency, respectively.

In cases where the reselected investigations needed

bridging arrangements, the Procurement Office

executed contract modifications. The SIMBIOS Project

provided bridging funds from the end date of the

previous contract to the start date of the new contract

(projected date was December 2000). SIMBIOS key

contract and project personnel to work on contracts are

shown in Table 2.1.

Agreements with the international team members

have being initiated by NASA HQ and are presently

administered by Dr. John Marra, Program Manager for

Ocean Biogeochemistry. These agreements should be

in place by Spring 2001.

Table 2.1 Contract evaluation key personnel

Contracting Officer: Lynne Hoppel

Contracting Assistant: Kathy Lingerfelt

Resource/Financial Officer: Patty Clow

Manager , SIMBIOS Project: Giulietta Fargion

Manager, Office for Global

 Carbon Studies: Charles McClain
SIMBIOS Project Annual Report

7

Figure 2.1. Global distribution of the NRA-96 selected SIMBIOS studies. United States (field): (1) Balch; (2) Brown/Brock; (3)

Capone/Carpenter/Subramaniam and Miller; (4) Carder and Green; (5) Chavez; (6) Cota; (7) Dickey; (8) Eslinger; (9) Frouin; (10) Miller;

(11) Mitchell and Green; (12) Müller-Karger; (13) Siegel; (14) Porter (15) Zaneveld and Mueller. United States (theoretical): Flatau; Siegel

and Stamnes/Chen. International: He; Korotaev; Kopelevich; and Li.

Figure 2.2. Global distribution of the NRA-99 selected SIMBIOS studies. United States (field): (1) Chavez; (2) Frouin; (3) Gao; (4) Harding; (5)

Miller; (6) Mitchell; (7) Morrison; (8) Nelson; (9) Siegel; (10) Spinhirne; (11) Stumpf; (12) Subramaniam; (13) Zalewski. United States

(theoretical, protocols, or team support): Gregg; Hooker; Maritorena; Mueller; Trees and Wang. International: Bohm; Zibordi; Fougnie;

Deschamps; Antoine; Kopelevich; Ishizaka; Fukushima; Chen; Li; He and Tang.
SIMBIOS Project Annual Report

8

Chapter 3

SIMBIOS Project Data Processing and Analysis Results

Ewa Ainsworth, Giulietta Fargion, Bryan Franz, Christophe Pietras and Paul Smith

SAIC General Sciences Corporation, Beltsville, Maryland

Sean Bailey, Joel Gales and Gerhard Meister

FutureTech Corporation, Greenbelt, Maryland

Kirk Knobelspiesse and Jeremy Werdell

Science Systems and Applications Inc., Greenbelt, Maryland

Menghua Wang

University of Maryland Baltimore County, Baltimore, Maryland

Charles McClain and Gene Feldman

NASA Goddard Space Flight Center, Greenbelt, Maryland

3.1 INTRODUCTION

The SIMBIOS Project is concerned with ocean color

satellite sensor data intercomparison and merger for

biological and interdisciplinary studies of the global oceans.

Imagery from different ocean color sensors can now be

processed by a single software package using the same

algorithms, adjusted by different sensor spectral

characteristics, and the same ancillary meteorological and

environmental data. This enables cross-comparison and

validation of the data derived from satellite sensors and,

consequently, creates continuity in ocean color information

on both the temporal and spatial scale. The next step in this

process is the integration of in situ ocean and atmospheric

parameters to enable cross-validation and further refinement

of the ocean color methodology. The SIMBIOS Project

Office accomplishments during 2000 year are summarized

under (a) satellite data processing, (b) data product

validation, (c) SeaBASS database, (d) supporting services,

(e) sun photometers and calibration activities and (f)

calibration round robins. These accomplishments are

described below.

3.2 SATELLITE DATA PROCESSING

3.2.1 MOS: Data Collection, Processing, and Distribution

Since February 1999, the SIMBIOS project has been

operating a receiving station at NASA's Wallops Flight

Facility (WFF) to acquire data from the German Modular

Optoelectronic Scanner (MOS) onboard the Indian IRS-P3

spacecraft. When a pass is acquired at Wallops, the raw files

are transferred to the SIMBIOS project at NASA's GSFC via

an automated FTP process. The raw files are then converted

to Level-0 format through a software package provided by

the Indian Space Research Organization (ISRO). The

resulting Level-0 files are made available to German Remote

Sensing Data Centre (DLR-DFD) for archive and

distribution. In addition, the SIMBIOS project processes the

data through Level-1B using the standard software provided

by the German Institute for Space Sensor Technology (DLRISST) (Neumann et al., 1995). All data processed by the

SIMBIOS project is made available through the MOS browse

system at SIMBIOS web page. The Level-1B data can be

processed to Level-2 using a SIMBIOS-developed software

tool that applies the standard SeaWiFS algorithms of Gordon

and Wang (1994). This Multi-Sensor Level-1 to Level-2

software (MSL12) is currently capable of processing data

from SeaWiFS, MOS, OCTS, and POLDER using identical

algorithms, and it has been applied in previous studies of

MOS-SeaWiFS cross-calibration (Wang and Franz, 1999).

MOS Intercomparison with SeaWiFS

The SIMBIOS project has recently developed a

prototype application to automate the sensor-to-sensor

intercomparison process using the online archive of MOS

data from Wallops and the SeaWiFS HRPT data from the

GSFC groundstation. MOS scenes are first reprocessed to
SIMBIOS Project Annual Report

9

Level-1B to ensure the latest calibration has been applied,

and then the meta-data of each 200 x 200 km scene segment

is examined to determine the observation time, geographic

area, and percentage of visible ocean pixels. If the scene

segment contains at least 80% ocean pixels and no more than

20% cloud pixels, the process proceeds to perform a database

search for potential SeaWiFS file matchups. Once a valid

SeaWiFS match is located, the area corresponding to the

MOS scene segment is identified in terms of pixel and scan

limits within the SeaWiFS scene, and both subscenes are

processed to Level-2 using MSl12. Some sample results from

this automated analysis are shown in Figure 3.1. The

histograms show the distribution of SeaWiFS (solid line) and

MOS (dashed line) water-leaving reflectance within each

match-up scene, for the spectral channels near 443, 490, and

510 nm. The selected scenes represent various locations off

the U.S. East Coast, for various times throughout years 1999

and 2000. These results demonstrate that the Gordan and

Wang (1994) atmospheric correction scheme, in conjunction

with a sensor cross-calibration, can provide comparable

water-leaving reflectance retrievals from two independent

observations from different sensors at different times of day.

With the recent catastrophic failure of the MOS onboard

calibrator unit, the SIMBIOS Project's ability to monitor the

relative calibration with SeaWiFS may prove to be of value

for future MOS calibration control.

Future Activities

We have already studied the relative differences between

MOS and SeaWiFS AOP retrievals when applying standard

Gordon and Wang techniques to both sensors (Wang and

Franz, 1999), and we used those results to develop a relative

calibration for MOS. Since that study, we have used our

archive of SeaWiFS and MOS data from Wallops to monitor

the relative stability of the AOP retrievals between the two

sensors, and they are generally in good agreement. The

SeaWiFS calibration is tied to the in situ data from the

MOBY bouy (Eplee et al., 2000). Using the same techniques

and the same atmospheric correction algorithms, we have

also developed a vicarious calibration between OCTS and

MOBY. We now plan to complete the circle and determine

the relative agreement between OCTS and MOS, thereby

placing some confidence level on the relative comparability

of OCTS and SeaWiFS. This information is important to

temporally merge the two global missions.

3.2.2. OCTS

At the request of, and in collaboration with NASDA, we

expect to acquire and process the entire OCTS GAC archive

from Level-1A through Level-3. Level-2 processing will be

performed using MSl12, with algorithms and output formats

nearly identical to standard SeaWiFS processing, and Level-3

processing will be performed using standard SeaWiFS space

and time binning software. Most of the science processing

software has already been updated and generalized for this

purpose, and verification of the Level-1A to Level-1B GAC

processing is in progress. Negotiations are underway with

the Goddard DAAC to archive and distribute the OCTS GAC

products.

3.2.3 Data Processing

Both OCTS and POLDER Level-1B data can now be

processed to Level-2 oceanic and atmospheric products using

the MSL12 software. In addition, SIMBIOS project has

developed a capability in MSL12 to process the OCTS

Level-0 to Level-1B data. The OCTS Level-1B data

processed with the MSL12 are usually in good agreement

with the NASDA standard Level-1B product. However,

there are some differences between satellite navigation

algorithms used in the two data processing protocols. For

OCTS bands 1-8, the radiance differences in a pixel by pixel

comparison between the two data processing protocols are

typically 0.54%, 0.53%, 0.54%, 0.85%, 1.00%, 1.62%,

2.20%, and 2.67%, respectively. These differences are

primarily due to improved navigation routines used in the

MSL12. Based on a study by Wang (1999), the MSL12

software is the implementation of the same standard

SeaWiFS atmospheric correction algorithm (Gordon and

Wang, 1994b) applyed to the OCTS, POLDER, and other

ocean color sensors (e.g., MOS). There are some major

improvements in MSL12 for the OCTS and POLDER data

processing concurrent with changes in processing algorithms

for the SeaWiFS 3rd data reprocessing in May 2000 (Wang,

2000b). Some specific atmospheric correction algorithm

improvements include:

• Improved Rayleigh lookup tables, in which the Rayleigh

(air molecule) scattering radiance is a function of the sea

surface wind speed, were generated and implemented in

the data processing system (Wang, 2000a) for OCTS,

POLDER, and MOS.

• A sun glint contamination correction algorithm was

developed and implemented in MSL12 (Wang and

Bailey, 2000).

• An iterative correction algorithm accounting for ocean

radiance contributions at wavelengths 765 and 865 nm

was developed and implemented in the data processing

step (Siegel et al., 2000).

• A correction algorithm for the sensor spectral band-pass

effects on the derived water-leaving radiances for

SeaWiFS (Wang et al., 2001) was developed. Similar

algorithms for both OCTS and POLDER were also

implemented in MSL12. Corrections of the sensor

spectral band-pass effects are necessary in order to have

meaningful Level-2 product comparisons derived from

two different sensors, e.g., OCTS and POLDER.

• Correction of the ocean whitecap radiance contributions

(Gordon and Wang, 1994a) has been updated (Wang,

2000b) with some current in situ measurements (Frouin

et al., 1996; Moore et al., 1998; Moore et al., 2000).
SIMBIOS Project Annual Report

10

Figure 3.1. Frequency distributions of percentage water leaving reflectance retrieved for both SeaWiFS (solid line) and MOS

(dashed line). Panel titles indicate year, day, longitude, latitude, and SeaWiFS band number.
SIMBIOS Project Annual Report

11

• The current 12 aerosol models used in the atmospheric

correction are: Oceanic RH=99%, Maritime RH=50%,

70%, 90%, and 99%, Coastal RH=50%, 70%, 90%, and

99%, and Tropospheric RH=50%, 90%, and 99%

(Wang, 2000b).

The updated MSL12 software significantly improves

derived ocean and atmospheric products.

3.3 PRODUCT VALIDATION

We are continuing our efforts in comparing and

analyzing ocean optical property data derived from both

OCTS and POLDER. One of the main objectives with this

effort is to understand and develop methodologies for

meaningful comparison and possible merging of data

products from multiple ocean color missions. In this section,

we briefly outline some major improvements in the OCTS

and POLDER data processing, the procedure of the sensor

vicarious calibration applied to both OCTS and POLDER

using the in situ MOBY data, and present and discuss some

comparison results derived from OCTS and POLDER

measurements.

Sensor Vicarious Calibration

It is well known that, for ocean color remote sensing, an

on-orbit vicarious calibration of sensor and algorithms is

necessary (Gordon, 1995). The vicarious calibration

technique has been successfully applied to SeaWiFS (Eplee

and McClain, 2000; Robinson and Wang, 2000) as well as

for the MOS and SeaWiFS product comparisons (Wang and

Franz, 2000). A vicarious calibration for OCTS and

POLDER using the in situ measurements from the Marine

Optical Bouy (MOBY) (Clark et al., 1997) was carried out.

First, 765 nm band vicarious calibration for OCTS and

POLDER was conducted using a similar method as that

applied to SeaWiFS (Robinson and Wang, 2000). Next, with

the derived 765 nm gain coefficient, the MOBY in situ

measurements were used for the calibration of visible bands

(Eplee and McClain, 2000). This coefficient is used to

calibrate the OCTS and POLDER visible bands such that the

derived normalized water-leaving radainces are equivalent to

the in situ measurements. The MOBY site has stable clearocean waters and is usually prevailed with maritime aerosols

and clear atmosphere. The MOBY program has therefore

been providing consistently high quality, clear-ocean optical

data. Five in situ measurements on Nov. 26, 1996, Jan. 13,

1997, Jan. 17, 1997, Feb. 16, 1997, and Feb. 23, 1997 were

used. Table 3.1 provides the derived gain coefficients

(except 670 nm band) for OCTS and POLDER. We are

currently in the process of finalizing the gain coefficients for

the 670 nm band.

Matchup Analyses

Reprocessing of the OCTS in situ matchups to data taken

from the SeaBASS database was performed. Matchup data

from Wallops Level-0 data and from Level-1B data files

supplied to the SIMBIOS Project by NASDA/EORC were

processed to Level-2 using MSL12 and the vicarious gain

coefficients used to process the Wallops data set. Therefore,

there are two OCTS data sets processed to Level-2. Files in

the GSFC Wallops archive that matched in situ data points

were processed from Level-0 (to Level-1B) to Level-2, while

Level-1B data files supplied to the SIMBIOS Project by

NASDA/EORC were processed to Level-2. The calibration

gain coefficients in Table 3.1 were used for both data sets.

The CNES has kindly provided the SIMBIOS project with

POLDER Level-1B data corresponding to the SeaBASS

database. For a given location (scene), POLDER can acquire

as many as fourteen near-contemporaneous measurements.

With the POLDER gain coefficients as in Table 3.1, the

normalized water-leaving radiances at the POLDER spectral

bands were derived from Level-1B data using the MSL12.

Table 3.1. The derived vicarious gain coefficients for OCTS

and POLDER spectral bands

Wavelength Vicarious Gain Coefficient

(nm) OCTS POLDER

412 1.12426 —

443 1.01539 1.06465

490 0.95084 1.02350

520 1.01784 —

565 1.03255 0.97541

765 0.92093 1.02946

865 0.89000 1.00000

 However, it was noted that, for a given scene, the

POLDER derived normalized water-leaving radiances

sometimes have high variations with various POLDER viewing

directions. Therefore, we used an averaging scheme to

eliminate the high variation points. For a given location, the

derived normalized water-leaving radiances from all possible

POLDER viewing directions are first averaged. Data points

which are within 50% variation in the first averaged value (i.e.,

0.5-1.5 of the average value) are then used to derive the final

mean value and considered as the POLDER derived normalized

water-leaving radiances for the scene. Figure 3.2 (a)-(c)

provide matchup results for the OCTS and POLDER. Fig. 3.2

(a) and 3.2 (b) are matchup results derived from OCTS

NASDA Level-1B and Wallops Level-0 data, while Fig. 3.2 (d)

shows results derived from POLDER CNES Level-1B data.

Each plot also gives a linear fit coefficient of slope, intercept

(Int), and the correlation coefficient (R). Note that the perfect

match corresponds to value of slope of 1, intercept of 0, and the

correlation coefficient of 1.
SIMBIOS Project Annual Report

12

0.1

1

0.1 1

 412 nm

 443 nm

 490 nm

 520 nm

OCTS [L

 565 nm

w

(λ)] N (mW / cm2 μm sr)

In Situ [Lw(λ)]N (mW / cm2

μm sr)

5

5

Processed with NASDA L1B data

Slope: 1.1638, Int: -0.0019, R: 0.9587

(a)

0.1

1

0.1 1

 412 nm

 443 nm

 490 nm

 520 nm

OCTS [L

 565 nm

w

(λ)] N (mW / cm2 μm sr)

In Situ [Lw(λ)]N (mW / cm2

μm sr)

5

5

Processed with SIMBIOS L0 to L1B software

Slope: 1.0386, Int: 0.0276, R: 0.9683

(b)

Figure 3.2 Sensor-derived normalized water-leaving radiances from (a) OCTS with NASDA Level-1B data; (b) OCTS with

Wallops Level-0 data.
SIMBIOS Project Annual Report

13

0.1

1

0.1 1

 443 nm

 490 nm

 565 nm

POLDER [Lw

(λ)] N (mW / cm2 μm sr)

In Situ [Lw(λ)]N (mW / cm2 μm sr)

5

5

Slope: 0.9303, Int: 0.0435, R: 0.9311

(c)

Figure 3.2 Sensor-derived normalized water-leaving radiances from (c) POLDER CNES Level-1B data compared with the

in situ measurements.

3.4 SEABASS INTERFACE

The SIMBIOS Project maintains the SeaWiFS Bio-optical

Archive and Storage System (SeaBASS) as an archive for in

situ products used in scientific analyses (e.g. product

verification and bio-optical algorithm development). Stored

data include measurements of water-leaving radiance,

chlorophyl a, and other related optical and pigment

measurements collected on ships, moorings, and drifters.

Additional information on SeaBASS is provided in Werdell et

al. (2000) and Hooker et al. (1994). A current description of

the SeaBASS system is also available via the World Wide

Web at http://seabass.gsfc.nasa.gov.

SeaBASS contains data from over 500 cruises,

encompassing more than 10,000 data files. Currently, data are

archived as simple, flat (two-dimensional) ASCII text with

standard metadata headers to fully define each file. The

headers contain information on date, time, location,

investigators involved, parameters collected, units, and other

related descriptive information. The SIMBIOS Project

designed the format to ensure that data files were simple, yet

comprehensive, globally compatible among different

computer platforms, and effortlessly ingested into the

database. Specific information and examples of the data

format may be referenced from the SeaBASS web site at

http://seabass.gsfc.nasa.gov/seabass_submit.html.

The data format has not changed significantly during the

past year of the SIMBIOS effort. Three metadata headers

were added: (1) 'measurement_depth' is the depth at which

discrete data were collected (e.g. bottle samples, and moored

and buoy data); (2) 'water_depth' is the bottom depth at the

site where data were collected; and (3) 'secchi_depth' is the

secchi depth at the site where data were collected. Two

metadata headers were made optional (i.e. they are no longer

required): (1) 'parameters' and (2) 'original_file_name'. A list

and description of each metadata header is available at

http://seabass.gsfc.nasa.gov/seabass_header.html.

The most significant change to the data format was the

standardization of the names and units used in the 'fields' and

'units' headers. Note that all data are accepted, but common

data types (e.g. AOP's, IOP's, pigments) are now required to

be abbreviated in a particular way and presented in specific

units. For example, (1) a column of upwelling radiance data

collected at 412.3 nm is required to be listed as 'Lu412.3' with

units of 'uW/cm2

/nm/sr'; or, (2) a column of HPLC-derived

chlorophyll a data is required to be listed as 'chl_a' with units

of 'mg/m^3'. A current list of standard SeaBASS field names

and units is available via the web at

http://seabass.gsfc.nasa.gov/cgi-bin/stdfields.cgi.

Protocols for format checking and data submission have

not been altered over the past year. The SIMBIOS Project

currently uses a PERL script feedback program known as

FCHECK to maintain the standard format for incoming data

files. Contributing researchers may test their data for

compatibility with SeaBASS from any platform by

electronically mailing their files to

fcheck@seabass.gsfc.nasa.gov. Once the data files meet

SeaBASS format requirements, the contributor may send their

data files and related documents (e.g. cruise reports and

calibration files) to the SeaBASS Admistrator via file transfer

protocol (FTP). The Administrator stores the files in their
SIMBIOS Project Annual Report

14

appropriate location in the archive and ingests the files into the

SeaBASS database. Password-secure web access to the data

files, the database, and supporting documentation is available

at the SeaBASS web site. For additional information on

format checking and data security issues refer to the SIMBIOS

Project 1998 Annual Report (McClain and Fargion, 1999) and

the SeaBASS web site.

The procedures for water-leaving radiance, normalized

water-leaving radiance, and chlorophyll a match-up analyses

changed little during the past year of the SIMBIOS effort.

Modifications were made to the processing software

concurrently with the third reprocessing of SeaWiFS data. In

particular, the Level 2 processing code was changed from

ANLY (l2gen) to the Multisensor Level-1 to -2 (MSL12)

program. Additional information on the match-up analysis

and protocols may be found in the SeaWiFS Postlaunch

Technical Report Series Volume 10 (Bailey et al., 2000).

 The architecture of the SeaBASS system will continue to

undergo major changes in the upcoming year. In the past year

of the SIMBIOS effort, a new SeaBASS relational database

was designed and built with the following modifications: (1)

an increase in the the number of tables to improved data

normalization; (2) a reconfigured system to take advantage of

multiple processors and increased physical storage space; (3)

the development of software for internal SIMBIOS Project

Office accounting purposes; and (4) the ability to store all data

and metadata in the relational database. The most visible

change to the user community will be how SeaBASS handles

queries via the World Wide Web. Currently, SeaBASS

database queries return a list of files that contain data or

metadata meeting the query criteria. Using the new database,

users may query for not only a list of files, but also the

physical data values meeting the query criteria. Search

engines taking advantage of this feature will be available to

the SeaBASS user community for pigment and atmospheric

data in Spring 2001.

3.5 SUPPORT SERVICES

In an effort to improve the quality and quantity of

calibration and validation data sets, the SIMBIOS Project

offers several support services to field investigators. These

services include; scheduling of on-board LAC recording for

SeaWiFS; overflight predictions for operational sensors

(currently SeaWiFS, OCTS, MOS-B, MODIS, OCI, and

OCM); near real time SeaWiFS imagery for cruise locations;

and sunphotometer instrumentation from a pool of projectowned instruments. These services may be requested via the

World Wide Web at http://simbios.gsfc.nasa.gov. In return for

these services, the SIMBIOS Project requests that the field

investigators provide in situ validation data to the Project's

bio-optical archive, SeaBASS. Since January of 2000, the

SIMBIOS Project has supported 52 cruises (Table 3.2).

Scheduling SeaWiFS On-board LAC Recording

Since much of the world’s oceans are not covered by a

SeaWiFS HRPT station, high-resolution data may be recorded

onboard the SeaWiFS sensor. As a service to the scientific

community, the SIMBIOS Project in conjunction with the

SeaWiFS Project can schedule SeaWiFS onboard LAC for

cruises that occur outside HRPT coverage. SeaWiFS has the

ability to record a maximum of 10 minutes of high-resolution

data per downlink. Typically, a 30-second interval is allotted

for LAC target, which corresponds to 180 scan lines or

approximately 200 km along track at nadir. Detailed

information on LAC scheduling is available on the SIMBIOS

web site.

Overflight Predictions for Operational Sensors

For calibration and validation purposes, in situ

measurements should be made as close to the sensor overflight

time as is possible. To aid investigators in determining when

sampling should occur, the SIMBIOS Project offers overflight

predictions for all operational ocean color remote sensors.

Currently, the sensors supported are SeaWiFS, MOS-B and

OCI, MODIS, and OCM. Detailed information on overflight

predictions is available on the SIMBIOS web site.

Near Real Time SeaWiFS Imagery

In addition to providing predictions for satellite overflight

times, the SIMBIOS Project offers near real time imagery of

the operational SeaWiFS products in JPEG or GIF format to

cruises at sea. 'True color' images are in JPEG format, all

other products are in GIF format. These images provide field

investigators with additional information with which they may

maximize in situ sampling of transient oceanographic features.

The default specifications for the images provided include:

• available LAC, HRPT, and GAC;

• chlorophyll-a and pseudo-true color images;

• 2-degree box about a designated location or the entire

designated region;

• image width of 600 pixels;

• minimum percent valid chlorophyll pixels: 5%;

• images may be customized to best accommodate

individual investigator needs.

Detailed information on near real-time imagery is

available on the SIMBIOS web site.
SIMBIOS Project Annual Report

15

Table 3.2 SIMBIOS supported cruises with services provided.

Cruise Principal Investigator Begin Date End Date On-board LAC Overflight Predictions Near Real Time

images

Instrument Pool

CalCOFI_0100 Greg Mitchell 1/6/00 1/28/00 •

Southern_Ocean Greg Mitchell 1/15/00 3/14/00 • ••

VOG_1 John N. Porter 1/23/00 1/30/00 •

Bahamas_Experiment Stanford Hooker 2/10/00 3/3/00 •• •

AMLR_99_00 Mati Kahru 2/18/00 3/16/00 •

NFRDI_0002 Mati Kahru 2/19/00 3/3/00 •

Modycot Jean-Noel Druon 2/22/00 3/11/00 •

ChesLight2000 Glenn Cota 2/23/00 3/31/00 •

EcoHab_FLT_GoM Bob Swift 3/1/00 3/9/00 •

Optics_Eilat Emmanuel Boss 3/10/00 5/7/00 • •

MODIS_MAB_FLT Bob Swift 3/10/00 3/24/00 •

Venice_Tower_Mar00 Jean-Francois Berthon 3/13/00 3/24/00 •

TC00_04 Carrie Leonard 3/20/00 4/29/00 •

Florida_Coast Stanford Hooker 3/20/00 4/30/00 • •

MODIS_SAB_FLT Bob Swift 3/25/00 3/27/00 •

Projeto_Revizee Aurea Ciotti 3/27/00 5/7/00 • •

AOL_GoM_Val_Flt Bob Swift 3/27/00 4/21/00 •

GOM2000 Richard Miller 4/2/00 4/15/00 • ••

RV Revelle_ASIAEX Michael Caruso 4/12/00 5/5/00 •

Scotia_Prince_Ferry William Balch 4/12/00 10/24/00 • ••

TC00_04 Carrie Leonard 4/14/00 4/30/00 •

Belgica_2000_11 Kevin Ruddick 4/17/00 4/19/00 •

TOTO_3 Robert Steward 4/19/00 5/2/00 • •

HarmBloom Steve Groom 4/28/00 5/6/00 •

LabradorSea Glenn Cota 5/1/00 6/10/00 •• •

Terengganu Tadris Ahmad 5/2/00 6/16/00 •

COBOP Robert Steward 5/10/00 6/6/00 •

NFR0005 Mati Kahru 5/22/00 6/1/00 •

Windy_CoOP Raphael Kudela 5/23/00 8/1/00 •

GlobecCCSMeso2000 Andrew Barnard 5/26/00 6/19/00 • •

Belgica_2000_15 Kevin Ruddick 6/5/00 6/7/00 •

Mucillagine Vittorio Barale 6/6/00 10/1/00 • •

Vattern_June_2000 Peter Land 6/7/00 6/8/00 •

Gulf_of_Oman Rick Miller 6/12/00 7/13/00 • •

KA_00_05 Francisco Chavez 6/19/00 7/9/00 • •

Adriatic Stan Hooker 6/23/00 7/24/00 •

Gulf_of_Maine_Class Bruce Monger 6/28/00 7/1/00 • •

LEO-15_Experiment Bob Arnone 7/1/00 7/31/00 •

SW_Florida Robert Steward 7/7/00 7/21/00 •

SSS Stephan Howden 7/10/00 9/1/00 •

METEOR_M48 Christian Schaefer-Neth 7/19/00 10/11/00 •

GLOBEC_Oregon Andrew Barnard 7/28/00 8/18/00 •• •

Chukchi_Sea Glenn Cota 8/1/00 8/31/00 •• •

OCE97_11168 Bruce Bowler 8/4/00 8/13/00 •

SeineSat2 Francis Gohin 8/30/00 9/25/00 •

Eastern_Mediterranean Sukru Besiktepe 9/15/00 9/30/00 •
SIMBIOS Project Annual Report

16

Belgica_2000_24 Kevin Ruddick 10/2/00 10/6/00 •

R_V_Meteor Ray Barlow 10/12/00 10/31/00 •• •

MiRIR_2 Richard Miller 10/20/00 11/3/00 • •

Michoacan2000 Mati Kahru 11/6/00 11/20/00 •

Big_Eye_Oceanog Carrie Leonard 11/7/00 11/21/00 • •

Venice_Tower_No J.F. Berthmore on 11/13/00 12/1/00 •

3.6 SIMBIOS SUNPHOTOMETERS

AND CALIBRATIONS

Atmospheric correction of satellite radiances and, in

particular, estimation of aerosol effects on the upwelling

radiance at the top of the atmosphere is one of the most

difficult aspects of satellite remote sensing. Merging of

aerosol properties obtained from in situ observations with

these derived by sensor algorithms creates exceptional

opportunities to validate and improve the atmospheric

correction. There are many uncertainties associated with in

situ measurements themselves. These uncertainties include sun

photometer or radiometer calibration and operational

problems, inadequate manual handling, and cloud

contamination. When matching against atmospheric properties

obtained by a satellite sensor, additional uncertainties come

into play. These uncertainties may be caused by different

satellite and surface instrument viewing angles and by

observation acquisition time discrepancies. In the case of the

atmosphere, these uncertainties are considerable. Therefore,

exact calibration of sun photometers and radiometers is

essential, as well as the best possible (and uniform from

instrument to instrument) correction of obtained

measurements. Finally, in order to cross-validate the quality of

in situ data, as well as extract measurements of high stability

and confidence, and compare them against satellite sensor

estimates with the largest degree of certainty, having multiple

observations from different sun photometers and radiometers

is essential.

3.6.1 Instrument Pool

The SIMBIOS Project provided funding to several of the

NRA-96 science team members for the purchase of in situ

ocean optical instrumentation. The funding was provided with

the stipulation that these instruments would be part of a

Project Office instrument pool for three years (1997-2000).

These instruments are to be returned to the science team

members and will not be part of the instrument pool after the

year 2000. The SIMBIOS Project has a sun photometer

instrument pool available for the new science team (NRA-99).

This pool currently consists of 12 MicroTops hand-held sun

photometers, 2 PREDE sun photometers (Japan), 2 SIMBAD

and 2 SIMBADA sun photometers, developed by the

Laboratoire d’Optique Atmospherique (LOA, France), as well

as 1 micro-pulse Lidar.

In addition to the sun photometer instrument pool, the

SIMBIOS Project takes advantage of the existing Aerosol

Robotic Network (AERONET), which is dedicated to

monitoring aerosol optical thickness around the globe.

Because most of the sun photometers used within the

AERONET project are in continental zones, the SIMBIOS

group enhanced this network with island and coastal stations.

Ten SIMBIOS CIMEL sites have been confirmed and

delivered so far and include Coconut Island (Hawaii--

replacing Lanai for management reasons) Ascension Island,

Bahrain, Papeete (Tahiti), Wallops Island (Virginia, USA),

Chinae (South Korea), Erdemli (Turkey), Puerto Madryn

(Argentina), and Horta (Azores). The implementation of the

site of Perth (Australia) has been delayed due to customs

authorization but is expected soon. The remaining two

SIMBIOS CIMELs are presently at Goddard Space Flight

Center. One instrument is a back up for Hawaii, the second

one is a back up for the other sites. Cruise experiments

handled by various SIMBIOS investigators allow for the

collection of in situ measurements from hand-held and

shipboard sun photometers. These measurements, with the

augmented AERONET network, provide the Project with

global data sets.

Sun Photometer Calibration: Non-Polarized Channels

The calibration of the sun photometers was described in

McClain and Fargion (1999). Details on the sunphotometers’

operations, calibrations and theoretical principles are posted at

http://simbios.gsfc.nasa.gov/Sunphotometers/calibration.html.

Twelve Microtops, two SIMBAD and two PREDE

instruments are calibrated before and after every cruise

deployment. The processing code is common to all sun

photometers. This code allows for inter-calibration with a

CIMEL as the reference instrument and is based on the time to

time voltage ratios. The first and last calibration for the

Microtops #3773, the SIMBAD #972306 and the PREDE

#PS090063 are shown in Table 3.3. With the V0 are displayed

the standard deviation ∆V0 corresponding to the atmosphere

variation during the calibration or due to the time difference

with the CIMEL reference. Clear days are selected as

“calibration” days if they have a variation ∆V0/V0 less than

1% (Table 3.3). We have found that the optics and filters of

the instrument can change dramatically after one year of use.

In the case of the PREDE #PS090064 the variation reached

5%. Several factors lead to the degradation of the calibration

and the effect strongly depends on the wavelength. Thus the

Project recommends a frequent schedule of calibrations for
SIMBIOS Project Annual Report

17

each field experiment in order to retrieve the most accurate

AOT.

 Sun Photometer Calibration: Polarized Channels

The calibration of the polarized version of the CIMEL sun

photometers was described in McClain and Fargion (1999).

The first polarized sun photometer (#191) of the SIMBIOS

Project was deployed in Erdemli (Turkey) and worked since

November 1999. The second polarized sun photometer (#162)

was deployed in Wallops (USA) in June 2000.

Above Water Radiometer Calibration

The SIMBAD radiometer is also an above water

radiometer (in addition to being a sun photometer). The optics

and filters are the same but the electronic gain is different. The

calibration is performed using a 6” integrated sphere at GSFC.

SIMBAD instruments were calibrated since August 1999.

Table 3.4 shows the reflectance per count obtained using six

lamps. The 6” integrated sphere is maintained and calibrated

monthly by the Calibration Facility group at Goddard

(http://spectral.gsfc.nasa.gov).

3.6.2 Extraction of in situ AOTs

The Project has recently implemented its own correction

strategy for instrument voltages corresponding to AOTs. The

approach ensures a uniform AOT processing for all

instruments making the AOTs comparable amongst the

instruments and between instruments and satellite sensor

AOTs derived by means of the atmospheric correction (see

chapter 6 and 11 in Fargion and Mueller, 2000). Also, the

method uses a consistent set of tuning variables, such as

ancillary data, concurrently applied for the correction of

satellite radiances. Therefore, some stages of the satellite and

in situ data processing are identical, contributing to increased

confidence in the match-ups.

 SeaWiFS Data Acquisition

The SeaWiFS aerosol optical thickness (AOT) data were

obtained by spatially co-locating a 25x25 pixel grid box

around the pixel containing the ground-based measurement

station, thereby providing a maximum of 625 SeaWiFS

retrievals in each matchup. A spatial homogeneity

(uniformity) test in the retrieved τ a ( ) 865 was then conducted

to screen thin cirrus and high altitude aerosol contamination

because the in situ and satellite measurements are often

looking through different atmosphere paths. Only those

satellite data sets that passed the spatial homogeneity test were

used for the matchup analyses.

In Situ Data Acquisition from Cimel

A select group of the ground stations from AERONET

were chosen for the matchup analyses. These instruments

were located at either coastal or island stations and were

operational for a reasonable length of time after SeaWiFS

went into operation. Table 3.5 provides the AERONET

station name, location (latitude and longitude), and the

corresponding responsible AERONET PIs. Currently, efforts

are underway to include additional AERONET stations. The

retrieval of data from AERONET has been automated to

facilitate the matchup analyses. Once per month, a script is

automatically run to access the AERONET database and

retrieve τ a data for the predetermined sites. For the matchup

purpose, the ground-based measurements from AERONET

were first reduced to include only those records that fall ±3 hr

of the SeaWiFS overpass for a given station. These records

include the aerosol optical thickness measured at the four

spectral wavelengths (440, 500, 670, and 870 nm).

Table 3.3. Top of Atmosphere (TOA) signals and standard deviations for the sun photometers determined by transfer

calibration from a calibrated CIMEL at GSFC between August 1998 and September 2000.

Microtops

#3773

440nm 500nm 675nm 870nm 940nm

08-20-1998 1238±7 988±4 1219±5 825±3 1929±11

06-09-1999 1238±4 987±4 1198±3 827±2 2178±13

09-20-2000 1242±5 984±4 1194±4 817±2 1958±21

SIMBAD

#97206

443nm 490nm 560nm 670nm 870nm

08-20-1998 391290±719 477288±2026 404124±165 420995±2039 305421±414

09-23-1999 376205±2369 464224±815 391526±854 416182±933 300000±2204

03-06-2000 382815±2311 465574±2362 382168±1231 408538±961 301005±469

PREDE

#PS090064

440nm 500nm 675nm 870nm 940nm

10-16-1998 1.36±0.003E-04 2.79±0.02E-04 3.52±0.02E-04 2.77±0.01E-04 2.61±0.01E-04

09-23-1999 1.30±0.002E-04 2.72±0.005E-04 3.40±0.01E-04 2.67±0.004E-04 2.67±0.02E-04
SIMBIOS Project Annual Report

18

Table 3.4. Calibration coefficient for SIMBAD # 06 and 09 between 1999 and 2000.

SIMBAD

#972306

440nm 490nm 560nm 675nm 870nm

08-12-1999 3.818±0.006 2.192±0.0015 2.432±0.002 4.229±0.002 8.029±0.002

01-14-2000 3.7650±0.0007 2.1339±0.001 2.3894±0.002 4.1096±0.002 7.6506±0.008

03-06-2000 3.6442±0.0025 2.1186±0.0010 2.4042±0.0016 4.1324±0.0024 7.7366±0.0044

08-18-2000 3.7942±0.0049 2.2376±0.0017 2.5963±0.0031 4.3958±0.0055 8.2380±0.0123

SIMBAD

#972309

08-12-1999 4.931±0.003 2.425±0.002 2.695±0.003 4.312±0.004 8.277±0.0025

01-14-2000 5.0134±0.003 2.3451±0.0002 2.6511±0.0006 4.2599±0.0003 7.9370±0.004

03-06-2000 4.7363±0.0022 2.2771±0.0002 2.6234±0.0010 4.2219±0.0011 8.0104±0.0057

As an initial quality control step, the data were averaged

and some variation parameters were computed to screen

possible cloud contamination. Only those data sets that had

low temporal variations (stable atmosphere) were then further

reduced to ±1 hr of the SeaWiFS overpass and used for the

matchup analyses. Usually, the CIMEL instruments routinely

take one measurement every 15 min near local noon;

therefore, for a given SeaWiFS file, there may be as many as

eight AERONET measurements that qualify as a match for the

2 hr time window. The strategy in the validation study is not

to compromise good data with bad data for the purpose of

more matchups, i.e., it is preferred to screen out some good

data to keep the high quality of data sets.

AOT Match-up Results

Match-ups between AOT data obtained from in situ

observations and satellite-derived AOT levels are analyzed to:

i) verify calibration of satellite sensor near-infrared bands and

ii) establish the effectiveness of the applied suite of aerosol

models. Match-ups have been obtained for the SeaWiFSderived aerosol properties and AOT levels calculated from

AERONET and SIMBIOS sun and sky radiometer data. AOT

calculated from satellite sensor observations are a by-product

of the atmospheric correction (Gordon, 1994). The match-up

results are not conclusive.

Table 3.5 AERONET sites used for aerosol matchup analyses.

Station Latitude Longitude AERONET PI

Bahrain 26.32 50.50 C. McClain*

Bermuda 32.37 -64.70 B. Holben

Dry Tortugas 24.60 -82.80 K. Voss & H. Gordon

Kaashidhoo 4.97 73.47 B. Holben

Lanai 20.83 -156.99 C. McClain*

SanNicolas 33.26 -119.49 R. Frouin

* SIMBIOS Project Office

Research into vicarious calibration of the SeaWiFS

instrument using in situ AOT observations and validation of

the atmospheric correction algorithm is continuing. The initial

scheme for elimination of cloudy and erroneous AOT

measurements was applied with CIMEL sun and sky

radiometer data (Wang, 2000). This strategy was based on

statistical estimates of AOT variations throughout a day and

only used one spectral band (870nm) of the CIMEL

radiometer to analyze those variations. Figure 3.3 illustrates

the results of comparisons between SeaWiFS-obtained AOT

values and in situ AOT data derived from CIMEL

measurements in 1998 and 1999. Four CIMEL sites are

included in the match-ups. In situ AOT results are converted

into SeaWiFS spectral bands and only those bands are

displayed which are the closest to the original CIMEL bands.

The slope and intercept of the linear fit are given at each

wavelength. AOT match-ups compare well for SeaWiFS and

in situ measurements in the red (670nm) and (865nm) bands.

The slope of the linear fit between the pairs of SeaWiFS and

in situ measurements closely approaches 1 for the 865nm band

match-ups. Therefore, the calibration of SeaWIFS in band 8

may be considered reasonable. Nevertheless, as the

wavelength decreases towards the visible spectrum, the slope

value for the match-ups decreased indicating that the SeaWiFS

algorithm underestimates AOT relative to CIMEL

observations. Two reasons for this underestimation are

considered: the relative and absolute calibrations of the

SeaWiFS near-infrared bands, and the SeaWiFS aerosol

models used to extrapolate the extracted near-infrared AOT

measures towards the visible spectra. Further studies are

needed to draw more precise conclusions about sensor and

atmospheric correction accuracies. Several investigations are

presented. The spectral distributions of daily AOT CIMEL

measurements obtained at the AERONET/SIMBIOS sites are

compared with the corresponding distributions of SeaWiFSderived AOT matchup points. In general, SeaWiFS AOT

spectral distributions appear to be defined by much flatter

curves than in situ AOT measures. This strongly contributes to

the inaccuracies in AOT matchups.

AOT Cross Calibration Experiment

Several sun photometers were deployed in early 1999

during the INDOEX experiment

(http://borneo.ucsd.edu/index.html). The instruments were
SIMBIOS Project Annual Report

19

used onboard the R/V Ronald H Brown ship sailing in the

Indian Ocean for more than 2 months. Aerosol optical

thicknesses were collected concurently using MicroTops sun

photometer, SIMBAD and shadowband radiometers. Cross

comparison between aerosol optical thicknesses obtained with

each radiometer are presented on Figure 3.4. AOT measured

by SIMBAD and shadowband radiometers are plotted versus

AOT measured by the MicroTops sun photometer. We have

limited the comparison to the in situ AOT collected within ±3

hr of the SeaWiFS overpass window. We allowed the

maximum difference of thirty minutes between AOT

observation time of each instrument. The maximum spatial

difference between geographical locations where each set of

AOT measurements was obtained is 15km.

A good agreement is shown at 870nm and is reasonable at

other channels. The channels of each instrument could be

slightly different, and so taking into account the spectral

dependance of AOT could result in a better comparison.

Nonetheless, promising comparisons between SeaWiFS and

the three sun photometers are expected.

3.7 CALIBRATION ROUND ROBIN

A new optical laboratory has been set up by the SIMBIOS

project. Its main purpose is to monitor the temporal stability of

the SeaWiFS Transfer Radiometer SXR-II. The light source is

a SeaWiFS Quality Monitor SQM-II manufactured by

Satlantic. The laboratory is especially setup for the SXR-II /

SQM-II configuration, but can also be used with 2 SQMs

installed simultaneously. The distance between light source

and radiometer can be varied from 0.2 to 1.2 m. The area

containing the SXR and the SQM is enclosed by black felt

curtains. Thus, light emitted from the remote instruments (PC

screens, temperature controller and voltmeter display) is

blocked, and only light from the SQM will be measured by the

SXR. Measurements of ambient light and SXR dark current

showed negligible differences, proving the effectiveness of the

light absorption by the felt curtains.

A study comparing two commercial SQM versions [one

from Satlantic Inc., one from Yankee Environ. System (YES)

Inc.] is currently underway in order to evaluate each

intruments’ stability. The study is based on data from the

above laboratory, data taken by the SIMBIOS team at YES

facilities and data from two previous SIMBIOS measurement

campaigns. Several experiments showed that the sensitivity of

the SXR-II shifted after undergoing shocks of a magnitude

that can be expected during shipping procedures. A problem

with the mirror fixtures inside the SXR was soon identified as

the most likely cause. During two repair sessions at Reyer

Corp., the mirror fixtures were fastened and epoxied.

Afterwards the SXR sensitivity remained stable: after

controlled shock treatments as well as regular shipping, the

sensitivity changed by less than 0.3%. The SXR-II was

calibrated using the Hardy-Sphere of NASA GSFC Code

920.1 Calibration Facility. The radiance of the SQM-II was

measured with the Code 920.1 spectroradiometer Optronics

746 (which was also used to calibrate the Hardy-Sphere) at

wavelength intervals of 10 nm from 410 nm to 1100 nm (10

nm bandwidth for each measurement). The SQM radiances are

shown in Figure 3.5 for three different light levels as measured

by the Optronics 746 in the Code 920.1 clean room, and as

measured by the SXR-II in the SIMBIOS laboratory. The data

from the different sensors agree well, with deviations ranging

from 0 to 10%, depending on wavelength. The strong

deviations of the spectra from the theoretical blackbody

Planck spectrum (e.g. the dip around 600 nm) are due to the

blue colored glass in front of the SQM-II, whose purpose is to

reduce the radiance in the red wavelength range in order not to

saturate underwater radiometers.

 In November 2000, the SXR-II was sent to NIST for

calibration using the newly developed SIRCUS (Spectral

Irradiance and Radiance Calibrations with Uniform Sources)

facilities, that use tunable lasers and integrating spheres.

Results are expected to be available by the end of the year.

3.8 SIMBIOS COMPUTING

RESOURSES

The SIMBIOS computing facility contains a variety of

equipment types. The main goal of the facility is to provide

computational support for the following areas:

• routine bulk data processing,

• large scale analysis and matchup between sensors,

• focused analysis on data subsets, and

• storage of large sensor data sets.

Co-location of the facility with the SeaWiFS Project

continues to benefit the Project by making available other

resources such as high-quality color printers, network

equipment, and other supporting equipment. Large-scale tasks

such as bulk data processing and large-scale analysis tasks are

performed by two Silicon Graphics (SGI) Origin 2000 servers.

Each system has 6 processors and approximately 3

gigabytes (GB) of RAM. The influx of large data sets for

additional sensors requires increasing amounts of disk space,

especially when performing comparative analysis. The Data

Processing server has 90 GB of disk space and the Data

Analysis server has 374 GB of total disk space. Long-term

storage of data sets is handled by the 6 TB tape library

attached to the Data Analysis server. Silicon Graphics O2

workstations continue to be the system of choice for software

development and focused analysis tasks. The sizing of the

workstations varies with the task requirements.

SIMBIOS is connected to the Internet via a 100-megabit

link, making network transfer of large data sets more feasible.

Internal connections are also 100 megabit for rapid transfer of

data between the servers and the workstations. Data ingest for

SIMBIOS is also done via removable media including CDROM, 8 millimeter tape, 4 millimeter (DAT) tape, and DLT

media up through DLT 7000. Off-line media storage space is

shared with the SeaWiFS Project.
SIMBIOS Project Annual Report

20

Figure 3.3. Match-up results between SeaWiFS-obtained AOT values and AOT measurements captured by different

CIMEL sun photometers for a - 443, b – 490, c – 670 and d – 870 nanometers. Data collected in four sites were used:

Bahrain, Bermuda, Kaashidhoo and San Nicolas Island.

The scope of the group's efforts expanded over the 1999

year, including the addition of the POLDER dataset,

installation of a ground station at Wallops Flight Facility to

receive MOS data downlinks, and routine processing of MOS

data at the Greenbelt site. In support of these efforts, the

facility has experienced moderate growth over the past year.

A workstation was added to the Project, as well as additional

local storage for the workstations and high capacity tape

drives for local backup and archival capability.

Each server was upgraded with a FibreChannel interface.

The new interface will allow the servers to participate in the

SeaWiFS Project Storage Area Network (SAN). Connection to

the SeaWiFS SAN will allow the SIMBIOS team to access

SeaWiFS data products and ancillary data at a rate of up to 1

gigabit per second. Participation in the SeaWiFS high

availability server cluster via Silicon Graphics (SGI) FailSafe

software is planned for next fiscal year. Membership in the

cluster will give the Project the capability of automatic

failover of services from one server to another in the event of

a server crash, maximizing resource availability.

REFERENCES

Bailey, S.W., C.R McClain, P.J. Werdell, B.D. Schieber,

2000: Normalized Water-Leaving Radiance and

Chlorophyll a Match-Up Analyses. In SeaWiFS

Postlaunch Technical Report Series, Volume 10, NASA

Tech. Memo. 2000-206892, S.B. Hooker and E.R.

Firestone, Eds. NASA Goddard Space Flight Center,

Greenbelt, Maryland, 59 pp.

Clark D.K., H.R. Gordon, K.J. Voss, Y.Ge, W. Broenkow, and

C. Trees, 1997: Validation of Atmospheric Correction

Over Oceans. J. Geophys. Res., 102, D14, 17,209-17,217.
SIMBIOS Project Annual Report

21

Figure 3.4. Cross comparison of AOT collected with MicroTops, SIMBAD and shadowband radiometers deployed

onboard the R/V Ronald H Brown ship during INDOEX experiment (1999). Square symbols represent the data

captured by SIMBAD and cross symbols represent AOT captured by shadowband radiometer. Comparisons are

made for MicroTops channels: a - 440, b – 500, c – 675 and d – 870 nanometers.

Fargion, G. S., and J. L. Mueller, 2000: Ocean Optics for

Satellite Ocean Color Sensor Validation, Revision 2,

NASA/TM-2000-209966, NASA Goddard Space Flight

Center, Greenbelt, MD, 184 pp.

Frouin, R., M. Schwindling, and P.Y. Deschamps 1996:

Spectral reflectance of sea foam in the visible and near

infrared: In situ measurements and remote sensing

implications, J. Geophys. Res., 101, 14,361-14,371.

Gordon, H.R., 1995: Remote sensing of ocean color: a

methodology for dealing with broad spectral bands and

significant out-of-band response, Appl. Opt., 34, 8363-

8374.

Gordon, H.R., and M. Wang, 1994a: Influence of oceanic

whitecaps on atmospheric correction of ocean-color

sensor, Appl. Opt., 33, 7754-7763.

Gordon, H.R., and M. Wang, 1994b: Retrieval of water-leaving

radiance and aerosol optical thickness over the oceans with

SeaWiFS: A preliminary algorithm, Appl. Opt., 33, 443-

452.

Hooker, S.B., C.R. McClain, J.K. Firestone, T.L. Westphal, En. Yeh, and Y. Ge, 1994: The SeaWiFS Bio-Optical

Archive and Storage System (SeaBASS), Part 1. In

NASA Tech. Memo. 104566, Vol. 20, S.B. Hooker and

E.R. Firestone, Eds., NASA Goddard Space Flight

Center, Greenbelt, Maryland, 40 pp.

Eplee, Robert E. Jr., Wayne D. Robinson, Sean W. Bailey,

Dennins K. Clark, P Jeremy Werdell, Menghua Wang,

Robert A. Barnes, and Charles R. McClain, 2000: The

Calibration of SeaWiFS, Part 2: Vicarious Techniques,

Submitted to Applied Optics.
SIMBIOS Project Annual Report

22

Figure 3.5. Radiances of the SeaWiFS Quality Monitor SQM-II (manufactured by Satlantic Inc.) for wavelengths from 410 nm

to 1100 nm. The solid lines show data measured by the Optronics 746, the triangles and squares show data measured by the

SXR-II. Figure produced by John Cooper of GSFC Code 920.1.

Eplee, Robert E. & C.R. McClain, 2000: MOBY Data

Analysis for the Vicarious Calibration of SeaWiFS Bands

1-6, Vol. 9, In NASA Tech. Memo. 2000-206892,

SeaWiFS Postlaunch Technical Report Series, S.B.

Hooker and E.R. Firestone, Eds., NASA Goddard Space

Flight Center, Greenbelt, Maryland, p43-50.

McClain, C.R,. and G.S. Fargion, 1999: SIMBIOS Project

1998 Annual Report, NASA Tech. Memo. 1999-208645,

NASA Goddard Space Flight Center, Greenbelt,

Maryland, 105 pp.

Moore, K.D., K.J. Voss, and H.R. Gordon 1998: Spectral

reflectance of whitecaps: Instrumentation, calibration, and

performance in coastal waters, J. Atmos. Ocean. Tech.,

15, 496-509.

Moore, K.D., K.J. Voss, and H.R. Gordon, 2000: Spectral

reflectance of whitecaps: Their contribution to waterleaving radiance, J. of Geophys. Res., 105, 6493-6499.

Neumann, A., Walzel, T., Tschentscher, C., Gerasch, B.,

Krawczyk, H, 1995: MOS-IRS Data Processing, Software

and Data Products (Preliminary Documentation), DLR

Institute for Space Sensor Technology.

Mueller, J.L., and R.W. Austin, 1995: Ocean Optics Protocols

for Validation, Revision 1, NASA Tech. Memo. 104566,

Vol. 25, S.B. Hooker, E. R. Firestone, and J. G. Acker,

Eds., NASA Goddard Space Flight Center, Greenbelt,

Maryland, 67 pp.

O’Reilly, J. E., S. Maritorena, B. G. Mitchell, D. A. Siegel, K.

L. Carder, S. A. Garver, M. Kahru, and C. McClain,

1998: Ocean color chlorophyll algorithms for SeaWiFS,

J. Geophys. Res., 103(C11), 24,937-24,953.
SIMBIOS Project Annual Report

23

Robinson, W., and M. Wang, 2000: Vicarious calibration of

the SeaWiFS band 7, In Vol. 9, NASA Tech. Memo.

2000-206892, SeaWiFS Postlaunch Technical Report

Series, S.B. Hooker and E.R. Firestone, Eds., NASA

Goddard Space Flight Center, Greenbelt, Maryland, p.38-

42.

Siegel, D.A., M. Wang, S. Maritorena, and W. Robinson

2000: Atmospheric correction of satellite ocean color

imagery: the black pixel assumption, Appl. Opt., 39,

3582-3591.

Wang, M. 1999: A sensitivity study of SeaWiFS atmospheric

correction algorithm: Effects of spectral band variations,

Remote Sens. Environ., 67, 348-359.

Wang, M., 2000a: Effects of the vicarious calibration on the

SeaWiFS derived aerosol optical properties, Proceedings

of Ocean Optics XV, Musée Océanographique, Monaco,

October 16-20, 2000a.

Wang, M. 2000b: The SeaWiFS atmospheric correction

algorithm updates, In Vol. 9, NASA Tech. Memo. 2000-

206892, SeaWiFS Postlaunch Technical Report Series,

S.B. Hooker and E.R. Firestone, Eds., NASA Goddard

Space Flight Center, Greenbelt, Maryland, p.57-63.

Wang, M., and S. Bailey 2000: Correction of the sun glint

contamination on the SeaWiFS aerosol optical thickness

retrievals, In Vol. 9, NASA Tech. Memo. 2000-206892,

SeaWiFS Postlaunch Technical Report Series, S.B.

Hooker and E.R. Firestone, Eds., NASA Goddard Space

Flight Center, Greenbelt, Maryland, p.64-68.

Wang, M., and B.A. Franz, 2000: Comparing the ocean color

measurements between MOS and SeaWiFS: A vicarious

intercalibration approach for MOS, IEEE Trans. Geosci.

Remote Sens., 38, 184-197.

Wang, M., B.A. Franz, R.A. Barnes, and C.R. McClain,

2001: Effects of the SeaWiFS spectral band-pass on the

retrieved ocean near-surface optical properties, Appl.

Opt., in press.

Wang M., S. Bailey, C. Pietras, C.R. McClain, and T. Riley,

2000: SeaWiFS Aerosol Optical Thickness Matchup

Analysis, In SeaWiFS Post launch Tech. Memo. Series,

Vol 10, 2000-206892, S.B. Hooker and E.R. Firestone,

Eds, NASA Goddard Space Flight Center, Greenbelt,

Maryland.

Werdell, P.J., S. Bailey, and G.S. Fargion, 2000: SeaBASS

Data Protocols. In Ocean Optics Protocols for Satellite

Ocean Color Sensor Validation, Revision 2, NASA

Tech. Memo. 2000-209966, G.S. Fargion and J.L

Mueller, Eds. NASA Goddard Space Flight Center,

Greenbelt, Maryland, 184 pp.

This research was supported by the

SIMBIOS Project Office

PEER REVIEWED PUBLICATIONS

Barnes, R., R. E. Eplee, and F. S. Patt, 1998: SeaWiFS

Measurements of the Moon. EurOpt Series, Sensors,

Systems, and Next-Generation Satellites II, SPIE, 3498,

311-324.

Barnes, R., R. E. Eplee, F. S. Patt, and C. R. McClain 1999:

Changes in the Radiometric Sensitivity of SeaWiFS

Determined from Lunar and Solar-based Measurements.

Applied Optics, 38, 4649-4669.

Fargion, G. S., C. R. McClain, H. Fukushima, J. M. Nicolas,

and R. A. Barnes, 1999: Ocean color instrument

intercomparisons and cross-calibrations by the SIMBIOS

Project. SPIE, 3870-68, 397-403.

Fargion, G. S., C. R. McClain, and R. A. Barnes, 2000: Ocean

color instrument intercomparisons and cross-calibrations

by the SIMBIOS Project (1999-2000). SPIE, 4135, 411-

420.

Mueller, J., C. McClain, R. Caffrey, and G. Feldman, 1998:

The NASA SIMBIOS Program, Backscatter, May.

Siegel, D. A., M. Wang, S. Maritorena, and W. Robinson,

2000: Atmospheric correction of satellite ocean color

imagery: the black pixel assumption, Applied Optics 39,

3582-359.

Wang, M., 1999: A Sensitivity Study of SeaWiFS

Atmospheric Correction Algorithm: Effects of Spectral

Band Variations. Remote Sens.Environm., 67, 348-359.
SIMBIOS Project Annual Report

24

Wang, M., 1999: Atmospheric Correction of Ocean Color

Sensors: Computing Atmospheric Diffuse Transmittance.

Applied Optics, 38, 451-455.

Wang, M., 1999: Validation study of the SeaWiFS oxygen Aband absorption correction: Comparing the retrieved

cloud optical thicknesses from SeaWiFS measurements,

Appl. Opt., 38, 937-944.

Wang, M. and B. A. Franz, 2000: Comparing the ocean color

measurements between MOS and SeaWiFS: A vicarious

intercalibration approach for MOS, IEEE Trans. Geosci.

Remote Sensing, 38, 184-197.

Wang, M., S. Bailey, and C. R. McClain, 2000: SeaWiFS

Provides Unique Global Aerosol Optical Property Data,

Eos, Transaction, American Geophysical Union, 81,

p197.

Wang, M., B.A. Franz, R.A. Barnes, and C.R. McClain,

Effects of the SeaWiFS spectral band-pass on the

retrieved ocean near-surface optical properties, Applied

Optics (In press).

Accepted

Holben, B.N., et al., 2001: An emerging ground-based aerosol

climatology: Aerosol Optical Depth from AERONET, J.

Geophys. Res.

Porter J. N., M. Miller, C.Pietras, C. Motell: Ship Based Sun

Photometer Measurement Using Microtops

Sunphotometer, J. Atmos. Oceanic. Technol.

Wang, M. and S. W. Bailey, Correction of the Sun Glint

Contamination on the SeaWiFS Ocean and Atmosphere

Products, Applied Optics.

Submitted

Eplee Jr. R.E., W.D. Robinson, S.W. Bailey, D.K. Clark, P.J.

Werdell, M. Wang, R.A. Barnes, and C.R. McClain, The

Calibration of SeaWiFS, Part 2: Vicarious Techniques,

Applied Optics.

Porter J. N., M. Miller, C.Pietras, C. Motell: Ship Based Sun

Photometer Measurement Using Microtops

Sunphotometer, J. Atmos. Oceanic. Technol.

Smirnov, A., B.N.Holben, O.Dubovik, N.T.O'Neill, T.F.Eck,

D.L.Westphal, A.K.Goroch, C.Pietras, and I.Slutsker,

Atmospheric aerosol optical properties in the Persian Gulf

region, Geophys.Res.Lett.

OTHER PUBLICATIONS

Bailey S.W., C.R. McClain, P.J. Werdell, and B.D. Schieber,

2000: Normalized Water-leaving Radiance and

Chlorophyll a Match-up Analyses. In SeaWiFS

Postlaunch Technical Report Series, Vol. 10, NASA

Tech. Memo. 2000-206892, S.B. Hooker and E.R.

Firestone, NASA Goddard Space Flight Center,

Greenbelt, Maryland.

Hsu N.C., W.D. Robinson, S.W. Bailey, and P.J. Werdell,

2000: The Description of the SeaWiFS Absorbing

Aerosol Index. In SeaWiFS Postlaunch Technical Report

Series, Vol. 10, NASA Tech. Memo. 2000-206892, S.B.

Hooker and E.R. Firestone, NASA Goddard Space Flight

Center, Greenbelt, Maryland.

McClain, C.R. and G.S. Fargion, 1999: SIMBIOS Project

1998 Annual Report, NASA Tech. Memo. 1999-208645,

NASA Goddard Space Flight Center, Greenbelt,

Maryland, 105 pp.

McClain, C.R. and G.S. Fargion, 1999: SIMBIOS Project

1999 Annual Report, NASA Tech. Memo. 1999-209486,

NASA Goddard Space Flight Center, Greenbelt,

Maryland, 128 pp.

Fargion, G.S. and J.L. Mueller 2000: Ocean Optics Protocols

for Satellite Ocean Color Sensor Validation, Revision 2,

NASA Tech. Memo. 2000-209966, NASA Goddard

Space Flight Center, Greenbelt, Maryland, 183 pp.

Robinson, W. D. and M. Wang, 2000: Vicarious calibration of

the SeaWiFS band 7, The SeaWiFS Postlaunch Technical

Report Series, Vol. 9, NASA Tech. Memo. 2000-206892,

S.B. Hooker and E.R. Firestone, Eds., NASA Goddard

Space Flight Center, Greenbelt, Maryland, p.38-42.

Robinson W. D., G. M. Schmidt, C.R. McClain, and P.J.

Werdell, 2000: Changes Made in the Operational

SeaWiFS Processing, In SeaWiFS Postlaunch Technical

Report Series, Vol. 10, NASA Tech. Memo. 2000-

206892, S.B. Hooker and E.R. Firestone, NASA Goddard

Space Flight Center, Greenbelt, Maryland.

Wang, M., 2000: The SeaWiFS atmospheric correction

algorithm updates, In SeaWiFS Postlaunch Technical

Report Series, Vol. 9, NASA Tech. Memo. 2000-206892,

S.B. Hooker and E.R. Firestone, Eds., NASA Goddard

Space Flight Center, Greenbelt, Maryland, p.57-63.

Wang, M., S. Bailey, C. Pietras, C. R. McClain, and T. Riley,

2000: SeaWiFS aerosol optical thickness matchup

analyses, In SeaWiFS Postlaunch Technical Report
SIMBIOS Project Annual Report

25

Series, Vol. 10, NASA Tech. Memo. 2000-206892, S.B.

Hooker and E.R. Firestone, Eds., NASA Goddard Space

Flight Center, Greenbelt, Maryland, p.39-44.

Wang, M., B. Franz, and R. Barnes, 2000: Analyses of the

SeaWiFS spectral bandpass effects, The SeaWiFS

Postlaunch Technical Report Series, Vol. 10, NASA

Tech. Memo. 2000-206892, S.B. Hooker and E.R.

Firestone, Eds., NASA Goddard Space Flight Center,

Greenbelt, Maryland, p.6-11.

Wang, M. and S. Bailey, 2000: “Correction of the sun glint

contamination on the SeaWiFS aerosol optical thickness

retrievals,” The SeaWiFS Postlaunch Technical Report

Series, Vol. 9, NASA Tech. Memo. 2000-206892, S.B.

Hooker and E.R. Firestone, Eds., NASA Goddard Space

Flight Center, Greenbelt, Maryland, p64-68, 2000.

Werdell P.J., S.W. Bailey, and G.S. Fargion, 2000: SeaBASS

Data Protocols and Policy. In Ocean Optics Protocols for

Satellite Ocean Color Sensor Validation, Revision 2, G.S.

Fargion and J.L. Mueller, NASA Tech. Memo.2000-

209966, NASA Goddard Space Flight Center, Greenbelt,

Maryland.

SIMBIOS PRESENTATIONS

Bailey S.W., P.J Werdell, C.R. McClain, and G.S. Fargion,

2000: Validation of SeaWiFS-derived Chlorophyll:

Comparisons at Several Spatial and Temporal Scales,

Oceanography from Space 2000, Venice, Italy,

Chou, M.D., P.K. Chan, and M. Wang, 1999: Global aerosol

radiative forcing derived from SeaWiFS-inferred aerosol

optical properties, to be presented at AGU Fall Meeting,

Dec. 13-17, San Francisco, California.

Fargion, G. S., C. R. McClain, H. Fukushima, J. M. Nicolas,

and R. A. Barnes, 1999: Ocean color instrument

intercomparisons and cross-calibrations by the SIMBIOS

Project. EOS/SPIE Symposium on Remote Sensing,

Florence, Italy.

Fargion, G.S., 1999: An Overview of SIMBIOS Project.

International Symposoium on Ocean color Remote

Sensing and Carbon Flux by CERES and JUWOC, Chiba,

Japan.

Fargion G. and C. McClain, 2000: NASA’s Sensor

Intercomparison and Merger for Biological and

Interdisciplinary Ocean Studies (SIMBIOS) Program,

SPRING AGU- Washington, DC

Fargion G. S., C. R. McClain and Robert A. Barnes, 2000:

Ocean Color Instrument Intercomparisons and CrossCalibrations by the SIMBIOS Project, Earth Observing

System V, SPIE, San Diego.

Fargion G. S. and C. R. McClain, 2000: Three Years of

Ocean Color Instrument Intercomparisons and CrossCalibrations by the SIMBIOS Project (1997-2000),

Conference on Remote Sensing of the Ocean and Sea Ice

VI, Barcelona.

Pietras C. M, E. Ainsworth , S. Bailey, C. Hsu, M. Wang,

Fargion G. and C McClain, 2000: Aerosol Properties from

Field Measurements Using Hand-Held and Automatic

Sun Photometers, Spring AGU, Washington DC.

Pietras C., E. Ainsworth, S.W. Baileyet al., 2000: Initial

Results of Validation of Satellyte Sensor Derived Aerosol

Optical Thickness Against In Situ Atmospheric

Observations Collected by the SIMBIOS Project, to be

presented at the Eighth International Symposium Physical

Measurements and Signatures in Remote Sensing,

Aussois, France, January 8-12, 2001.

Wang, M., 2000: Effects of the vicarious calibration on the

SeaWiFS derived aerosol optical properties, The AGU

Spring Meeting, Washington, DC.

Wang, M., 2000: Effects of the Sea Surface Wind Speed on

the SeaWiFS Derived Ocean Color Optical Property Data,

Proc. the Ocean Optics XV, Musée Océanographique,

Monaco.
SIMBIOS Project Annual Report

26

Chapter 4

Validation of Surface Bio-Optical Properties in the Gulf of Maine

as a Means for Improving Satellite Primary Production Estimates

William M. Balch

Bigelow Laboratory for Ocean Sciences, West Boothbay Harbor, Maine

4.1 INTRODUCTION

One of the greatest challenges in providing sea-truth data

for various ocean color sensors is climatology. This is

particularly true in the Gulf of Maine since it is cloudy and

foggy more than it is clear; the climatology shows on average,

about 1 in 4-5 days has clear skies with clear days slightly

more frequent in the late summer and early fall. Our strategy

has been to use a ship of opportunity where one has choice of

the sampling days. This provides much better flexibility to

sample during clear periods with good satellite coverage. Our

SIMBIOS contract has been to use the M/S Scotia Prince ferry

as a ship of opportunity, running between Portland, Maine and

Yarmouth, NS. Measurements include continuous, surface,

along-track fluorescence, two independent measures of

backscattering, total light scattering, absorption, beam

attenuation, above-water remote sensing reflectance, calcitedependent light scattering, temperature, and salinity. XBT

drops allow acquisition of vertical temperature information,

useful for defining isopycnal slope, which affects primary

production. These data are comparable to a previous program

from early 1982, where a ship of opportunity program (SOOP)

was run on the truck ferry, M/V Marine Evangeline, which

ran along the same transect (Boyd, 1985). These surface data

were combined with satellite-derived sea surface temperature

fields to examine the Maine coastal current (Bisagni et al.,

1996). Unfortunately, this program stopped in 1982. The

ongoing SIMBIOS results will dovetail nicely with the

previous work (which also had CZCS coverage) for looking at

any long-term changes in the Gulf of Maine hydrography, biooptics, and biogeochemistry.

The Gulf of Maine is an ideal site for such a SOOP

program. It is a semi-enclosed basin with an average depth of

~150m. Isopycnal slope (and productivity) appears to be

strongly topographically driven, with the water column

becoming isothermal at the 60m isobath around Georges

Bank. The dynamic range of integrated productivity in the

Gulf of Maine varies seasonally by a factor of 45X (0.1 to 4.4

gC m-2 d-1; (O'Reilly and Busch, 1984; O'Reilly et al.,

1987)), greater than in the California Current (only15X

variability; 0.2-3.0 gC m-2 d-1). The major east coast data set

(MARMAP data set), also long in duration, unfortunately,

cannot maintain measurements of pigments or inherent optical

properties, thus this SIMBIOS project provides unique data for

the region. 

4.2 RESEARCH ACTIVITIES

We completed the following during the third year:

• May-October 2000 Twelve cruises aboard M/S

Scotia Prince completed between Yarmouth,

Nova Scotia and Portland, ME, as required by

our contract.

• Initial data submissions to be followed by more

results as discrete analyses of POC/PON, PIC,

and cell counts are completed. The final cruise

of this year was completed in 30 September,

2000; the following raw data are being worked

up for distribution to the NASA SIMBIOS

Project as soon as all post cruise calibrations are

finished.

Our field measurements include surface flow-through biooptics, and above-water optical measurements, hydrographic

measurements, discrete samples for calibration.

The M/S Scotia Prince Ferry

The M/S Scotia Prince ferry is 472 ft long, it carries up to

1500 passengers and 220 cars, displaces 11,968 gross metric

tons, and maintains18-20 kts speed in sea states up to Force 7.

In order to use this ship, we call 2-3 d ahead of each targeted

cruise date, when we see high pressure weather systems

moving into the area. The ship operates between 15 May and

31 October, departing Portland, ME each day at 2100h EDT,

and arriving in Yarmouth, NS at 0800 EDT, the next morning

(11 h total steam). Following a 1 hour layover in Yarmouth,

the ship departs at 0900h EDT, and returns to Portland by

2000h EDT, with another 1h layover before its next trip. The

ferry company (Prince of Fundy Cruises Limited) installed a

through-hull, non-toxic sample line for us to draw water. We

park our mobile laboratory 30m aft of the sample pipe on the

starboard side.
SIMBIOS Project Annual Report

27

Typical Cruise Operation

After the ferry leaves Portland Harbor, it goes to Witch

Rock, then takes a course of 87o (true bearing), passing just

north of Jeffreys Bank, then straight across Jordan Basin, to

German Bank and Cape Forchu (Yarmouth entrance). During

the evening leg to Yarmouth, we connect the many power,

data, and antennae cables from the laboratory to the ship,

ready the lab’s air handling equipment, mount the radiometers,

perform instrument calibrations, check seawater flow, and

generally prepare for the return transit the next morning (plus

a few hours sleep). Water flow is started ~ 1/4-1/2 hour after

departure from Yarmouth and data collection begins at the

Cape Forchu marker. The length of the transect is ~330 Km,

with ~290-330 km of effective data collection.

Flow-Through System

Our flow-through system makes a suite of IOP

measurements which are used to estimate the standing stock of

POC and PIC continuously. A suite of AOP measurements

from the bow allow direct verification of satellite radiance and

chlorophyll estimates (the latter being directly compared to

acetone extracts of chlorophyll). Data integration and logging

are done by National Instruments LabVIEW software, run on

three pentium processor computers. The system receives time

and the ship's geographic position continually from a Garmin

220 global positioning system.

Sea water first enters a vortex de-bubbler (SUNY VDB1),

then into a second 1.2m tall de-bubbler, equipped with a 1mm

screen to keep the largest zooplankton and salps out of the

optical instruments. (Entry of >1mm plankton into either one

of the optical tubes of the ac-9 can cause poor data quality

when comparing absorption and attenuation). Next, the flow

enters an InterOcean TempSal thermal conductivity sensor. It

measures salinity with an accuracy of +0.05 Practical Salinity

Units and temperature to an accuracy of +/-0.1o

C.

Temperature and salinity data also are used in the ac-9 postcruise calibration. Next, the flow bifurcates into a Turner

10AU-005-CE fluorometer for monitoring underway

chlorophyll fluorescence. The fluorometer is equipped with a

daylight white F4T5D lamp, blue-violet excitation filter (peak

excitation 438nm with half band pass of 340-500nm) and red

emission filter (high pass >665nm interference filter). To

insure against bio-fouling, the fluorometer is cleaned

regularly.

The water then passes through a tertiary debubbler via

metering pump, into a Wyatt Technologies Model Dawn F

laser light scattering photometer for measurements of volume

scattering. The flow-rate through this instrument is ~11-15

ml min-1. The photometer operates with a10 mW Argon ion

laser (514 nm) which is directed into the center of a flowthrough cuvette, whereupon, sea water is viewed by 15

photodiodes arranged between 21.54o

 and 158.14 o. Two

additional photodiodes are for laser power monitoring (one in

front of the viewing cuvette, and one after the cuvette). The

laser beam has a 1/e2

 gaussian beam profile radius of 0.39 mm

which makes the effective viewing volume of the lightscattering photometer 0.25 ml. All detectors are scanned at

rates up to 400 HZ. For most flow-through applications, we

slow the scanning rate to 200 HZ in order to not sample the

same sea water volume twice, thus preventing pseudoreplication.

The LabVIEW software, which controls the Wyatt light

scattering photometer, can be programmed to calculate

averages and standard deviations of the sea water volume

scattering function (VSF) over any desired time period.

Backscattering of particulate and dissolved material (bb p+g) is

calculated by integrating the VSF in the backwards direction

(Fig. 2C; see Balch et al.(1999)). For field applications, we

typically average the data for ~50s (which represents an

effective volume viewed of 9-12.5 ml). The statistics are

highly informative for understanding the optical variability of

different particle types. Values of bb p+g are well correlated to

POC.

Because of our interest in suspended PIC from

coccolithophores, we also measure volume scattering before

and after dissolution of CaCO3. Following the first 50s of

measurements on raw sea water, another peristaltic pump is

activated by the LabVIEW control system, which injects 0.5%

glacial acetic acid into the flow stream and mixes it in a

Teflon mixing coil. This drops the measured pH to ~5.8 to

dissolve all CaCO3. Once the pH stabilizes at the more acidic

value, volume scattering is re-sampled and average

backscattering re-calculated. The difference between the raw

and acidified backscattering values represents the "acid-labile"

backscattering (bb’). Using field measurements, we have

calibrated this acid-labile backscattering to atomic absorption

estimates of suspended calcite concentration (r2

=0.83). The

time for a complete acidification cycle can be adjusted, but we

typically run one complete raw/acidification cycle every 4

min. This means that during any passage, we would be

averaging data over ~2.2 km. For sea-truth measurements,

this is adequate since typically a 3 pixel by 3 pixel area from

SeaWiFS (10.9 km2

) is averaged for comparison with ship

results.

Water next flows into an “ac-9” (Wet Labs, Oregon). This

instrument simultaneously measures beam attenuation and

absorption at nine wavelengths using a dual path optical

scheme. The factory-stated accuracy of the attenuation and

absorption measurements is +0.005m-1 with linearity error of

+0.1%. Pure water values are subtracted in order to derive

total particulate and dissolved attenuation (cp+g) and

absorption (ap+g) which are then used to derive total particulate

and dissolved scattering bp+g (= cp+g - ap+g). An in-line

solenoid valve system, upstream of the ac-9, directs flow

through a filtration manifold every 2 min., which removes

particulate matter bigger than 0.2μm. After another 2 minutes,

flow reverts back to unfiltered sea water. Over each 4 minute

segment of the voyage, the ac-9 measures the inherent optical

properties (a, c and b) of total particulate and dissolved

material, as well as just dissolved organic matter (Fig. 4.1
SIMBIOS Project Annual Report

28

f,g,h). The difference between the two provides the IOPs of

the particles only. This filtration manifold saved us from

having to purchase and intercalibrate a second ac-9. As stated

before, cp+g and bp+g are well correlated to POC.

Flow-through surface water then is viewed by a Hobi Labs

HydroScat-2. This instrument is set to view an enclosed, 20-

liter, sand-blasted, stainless steel container (painted flat black

within). This vessel has a retractable brush inside to sweep

bubbles from the viewing window. The instrument measures

volume scattering at 140o

 and extrapolates to backscattering

using an assumed volume scattering function (Maffione and

Dana, 1997). It estimates bb (470 nm and 676 nm; Fig. 4.2b)

plus chlorophyll fluorescence, and allows estimation of the

wavelength dependence of bb.

The water finally is run through a FlowCAM (Sieracki et

al., 1998) which directs particles through an optical sensing

volume (several μl), where it measures chlorophyll and

phycoerythrin fluorescence of each particle and particle

silhouette (using frame grabbing technology). The image

analysis software then uses the particle silhouette to estimate

equivalent spherical diameter. Data for each triggering

particle are stored for later analysis. FlowCAM derived

particle concentrations are within 25% of manual count

methods and plots of manual vs. FlowCAM counts have an r2

of 0.96. Flow CAM particle sizes are highly correlated

(r2=0.996) to manual particle size estimates and derived

particle sizes of beads are generally within 2% of the nominal

bead size (Sieracki et al., 1998) with resolution down to ~5μm

diameter. This instrument provides major advances in our

understanding of the size spectrum of particles, as well as the

specific species that dominate the algal community along the

ferry track, all in real time. A FlowCAM is in constant

operation off the Bigelow Laboratory dock. Real-time image

data can be viewed at http://www.bigelow.org/flowcam/.

Particle volume is well correlated to cellular POC content.

Apparent optical properties

Water-leaving radiance and downwelling irradiance (for

calculating remote sensing reflectance) is measured from the

M/S Scotia Prince ferry using a Satlantic SeaWiFS Aircraft

Simulator (SAS). This consists of a down-looking radiance

sensor and a sky-viewing radiance sensor, both mounted on

the bow (Fig. 4.1d,f). An irradiance sensor is mounted on the

compass deck, aft of the bridge, far from any potentially

shading structures. The radiance detector views the water at

40o

 from nadir as recommended by the NASA SeaBOARR-98

report (Hooker et al., 1999). The distance of the sensor to the

water is ~8m. The water radiance sensor can view over an

azimuth range of ~200o

 and nadir range of 0-90o. The

direction of the sensor is changed manually, as the sun's

position changes, so the sensor is viewing the water 90 o from

the sun's azimuth, to minimize sun glint. Protocols for

operation and plaque calibration are made according to

SeaWiFS Technical Memorandum #25 (Mueller and Austin,

1995) and the NASA SeaWiFS SEABOARR Experiment

(Hooker et al., 1999). All data are logged at 16Hz. Before

1000h and after 1400h, data quality is poorer as the solar

zenith angle increases. Post-cruise, the 16Hz data are filtered

to remove as much residual white cap and glint as possible

(we accept the lowest 5% of the data). These data are in good

agreement with the SeaWiFS normalized water-leaving

radiances Fig.4.2d and i). In addition, we also take Microtops

sun photometer measurements when the sun is visible, in order

to estimate aerosol optical depth.

Hydrographic Information

Temperature and salinity of the surface flow stream is

measured with an InterOcean thermal conductivity sensor. It

measures salinity with an accuracy of +0.05 Practical Salinity

Units and temperature to an accuracy of +0.1o

C. (Fig.

4.2a)Hourly XBT profiles are used to construct one

temperature section per trip from which we can calculate

isotherm slope (strongly related to gradients in integrated

primary production and chlorophyll). 

Calibration

Calibration of the Satlantic radiance sensors is done

annually. We also check the calibration of the radiance

sensors using a 1.8% reflectance Spectralon plaque. Typical

variability of the plaque values was ~+/- 0.2-0.3%. The ac-9

was given a dry calibration at the beginning of each cruise, a

wet calibration (using 0.2μm filtered, de-ionized water) at the

beginning and end of each cruise, as well as annual factory

calibrations. These data showed that the absorption

calibrations were good to ~0.005 m-1 and the c calibrations

were good to ~0.01-0.02 m-1. The Wyatt light scattering

photometer calibration was checked before and after each

cruise with 0.02μm pore-size filtered HPLC-grade distilled

water. The water backscattering calibrations were good to

within +/-1 x 10-4 m-1. Absolute calibrations of the 90o

detector were performed with methanol and the calibration of

the remaining detectors was done (relative to the 90o

 detector)

using Dextran solution, which scatters isotropically.

Comparison of pre- and post-cruise calibrations of the ac-9

and Wyatt light scattering photometer allowed us to account

for instrument bio-fouling. The Hobi Labs Hydroscat 2

received annual factory calibrations.

Discrete Samples

Hourly, a surface water sample is taken for suspended

CaCO3, particulate organic carbon, chlorophyll, and

microscope counts. The technique of Fernandez et al. (1993)

is used to measure CaCO3 concentrations. Briefly, 100 ml

samples are filtered onto 0.4μm pore-size polycarbonate filters

and rinsed first with filtered sea water, then borate buffer

(pH=8) to remove sea water calcium chloride. Later, filters are

placed in trace metal free centrifuge tubes with 5 ml 0.5%

Optima grade nitric acid and the Ca concentration is measured
SIMBIOS Project Annual Report

29

using an inductively-coupled plasma optical emission

spectrometer (Scripps Inst. of Oceanography Analytical

Facility). Chlorophyll and particulate organic carbon samples

are filtered through H/A and baked GF/F filters, respectively,

and measured according to the JGOFS protocols (JGOFS,

1996). We participated in the SIMBIOS-sponsored round

robin on pigment measurements. A sampling of SeaWiFSderived chlorophyll a versus ship-derived chlorophyll a for

Gulf of Maine samples is shown in Fig. 4.2i. The OC-4

algorithm(O'Reilly et al., 1998) has made a dramatic

improvement over the OC-2 algorithm in accuracy of

chlorophyll predictions, but there is still a general

overestimate by SeaWiFS (Fig. 4.1e).

Microscope enumeration of coccolithophores and

coccoliths is done by filtering a 50ml water sample through a

Millipore HA filter, rinsed with borate buffer, and frozen in a

petri dish until counted (Haidar and Thierstein, unpublished-a;

Haidar and Thierstein, unpublished-b). The filter is placed on

a glass microscope slide, and 60o

C Canada Balsam is placed

on top of the filter, followed by a cover slip. The clarified

filter is examined with an Olympus BH2 microscope equipped

with polarization optics. Birefringent coccoliths and plated

coccolithophores can then be counted. For statistical reasons,

200 coccoliths or cells are counted from each sample, when

available.

Data manipulation

The types of continuous underway data collected during

the Gulf of Maine SIMBIOS program are summarized in

Table 4.1 with their appropriate units:

Table 4.1- Summary list of variables collected from the proposed Gulf of Maine test-site data set.

Underway Variable Unit Details

time decimal calendar day (EDT and GMT)

latitude, longitude o

N, oW Logged on GPS

temperature o

C

salinity PSU

total upwelling radiance μW cm-2 nm-1 sr-1 412, 443, 490, 510, 555, 670 & 685 nm

downwelling irradiance μW cm-2 nm-1 412, 443, 490, 510, 555, 670 & 685nm

sky radiance μW cm-2 nm-1 412, 443, 490, 510, 555, 670 & 685nm

fluorescence mg chl a m-3 calibrated to hourly discrete chlorophyll samples;

absorption m-1 412, 440, 488, 510, 555, 630, 650, 676 and 715 nm; partic & dissolved

attenuation m-1 412, 440, 488, 510, 555, 630, 650, 676 and 715 nm; partic & dissolved

scattering(derived) m-1 412, 440, 488, 510, 555, 630, 650, 676 and 715nm; partic & dissolved

backscattering (total/acid-labile) m-1; total bb at 470, 514 and 676 nm; acid labile backscattering at 514nm.

particle size spectrum μm size histogram and slope, calculated each 1/2h of cruise

particle image files particle collages available for each 1/2h of cruise

Hourly Depth Profiles

Temperature o

C XBT profiles

Maintenance of Public Database:

All of our data from the previous year have been released

to SEABASS. The data from the 2000 ferry trips are being

finalized now, with final checks of vicarious calibrations.

They will be released to SEABASS shortly. A summary of

the number of data points and satellite match-ups are given in

Table 4.2.

4.3 CONCLUSIONS

We exceeded our cruise requirements outlined in our

original contract (performing 42 days of validation work, not

40). The additional two trips were funded by NOAANESDIS. We anticipate no problems in timely data work-up

and submission as our processing software is well streamlined

for post-cruise calibrations. We requested a no-cost extension

in order to complete all the post-cruise calibration checks.

Moreover, with three years of sampling completed, we will

begin the summary publications about our findings. We will

ship the Microtops radiometer back to Goddard soon, now that

our third, and final field season is completed. The personnel

of the Prince of Fundy, Ltd ferry company have been superb

to work with; none of this work could have been accomplished

without their exemplary “can-do” attitude. They installed a

non-toxic, stainless steel pipe off of their sea chest, so we did

not have to install our sampling arm during year three (the

sample arm performed well for 30 trips, or ~9 thousand km

with only one failure). This streamlined our operation

significantly in the last year. Over the 42 trips, we achieved

an average of ~83% success rate in sampling under clear skies.

In all, we provided ocean color sea-truth data over 13,230 km,

with over 3,600 satellite match-ups (Table 4.3).
SIMBIOS Project Annual Report

30

Table 4.2 Summary of Gulf of Maine data taken from M/S Scotia Prince, over 3 years of Balch NASA SIMBIOS contract.

Each measurement of optical properties, hydrography, or chlorophyll represents an average over ~2.2 kilometers of horizontal

distance.

Activity 1998 1999 2000* Total

# cruises 10 20 12 42

#Lw,Ed SAS 867 3116 1440 5423

#ac-9 1388 2917 1440 5745

#bb(Wyatt) 1460 3290 1440 6190

#bb (HS-II) 0 3290 1440 4730

#Temp/Sal 1460 3290 1440 6190

#XBT Profiles(1/h) 85 171 96 352

#chl’s 1421 3290 1440 6151

# chl Match-ups (+/-6h)

 with SeaWiFS** 760 1779 1080 3619

Note: *Data values for 2000 are best estimates (final calibration checks and data quality checks are being done over the next

few months); ** Includes only SeaWiFS match-ups. Does not include MODIS match-ups, which may double the 2000 value.

Table 4.3 List of cruises and deliverables for FY’00 Balch SIMBIOS contract.

Cruise

Location

Ferry Cruise #Date (2000)FluorescenceBackscatteringWyatt&Hobi Labs Calcite-dependentbackscattering AbsorptionScatteringTemperatureMicrotops sunphotometerStationsSuspended CaCO3Chlorophyll POC/PON Conc. Coccoliths & CoccolithophoresFlow Cam (spp & size)XBT’s Lt, Lsky, Ed, & nLw (Satlantic SAS)

Portland, Maine and

Yarmouth (NS) 1 4 June ✓ ✓ ✶ ✓ ✓✓✓ ✓ 8 ✓ ✓✓ ✓ ✓ ✓ ✓

Portland, Maine and

Yarmouth (NS) 2 13

June ✓ ✓ ✓ ✓✓✓ ✓ 8 ✓ ✓✓ ✓ ✓ ✓ ✓

Portland, Maine and

Yarmouth (NS) 3 20

June ✓ ✓ ✓ ✓✓✓ ✓ 10 ✓ ✓✓ ✓ ✓ ✓ ✓

Portland, Maine and

Yarmouth (NS) 4 21

June ✓ ✓ ✓ ✓✓✓ ✓ 8 ✓ ✓✓ ✓ ✓ ✓ ✓

Portland, Maine and

Yarmouth (NS) 5 24

June ✓ ✓ ✓ ✓✓✓ ✓ 9 ✓ ✓✓ ✓ ✓ ✓ ✓

Portland, Maine and

Yarmouth (NS) 6 2 July ✓ ✓ ✓ ✓✓✓ ✓ 10 ✓ ✓✓ ✓ ✓ ✓ ✓

Portland, Maine and

Yarmouth (NS) 7 11

July ✓ ✓ ✓ ✓✓✓ ✓ 10 ✓ ✓✓ ✓ ✓ ✓ ✓

Portland, Maine and

Yarmouth (NS) 8 12

July ✓ ✓ ✓ ✓✓✓ ✓ 9 ✓ ✓✓ ✓ ✓ ✓ ✓

Portland, Maine and

Yarmouth (NS) 9 22

Aug ✓ ✓ ✓ ✓✓✓ ✓ 9 ✓ ✓✓ ✓ ✓ ✓ ✓

Portland, Maine and

Yarmouth (NS) 10 29

Aug ✓ ✓ ✓ ✓✓✓ ✓ 9 ✓ ✓✓ ✓ ✓ ✓ ✓

Portland, Maine and

Yarmouth (NS) 11 14

Sept ✓ ✓ ✓ ✓✓✓ ✓ 9 ✓ ✓✓ ✓ ✓ ✓ ✓

Portland, Maine and

Yarmouth (NS) 12 30

Sept ✓ ✓ ✓ ✓✓✓ ✓ 8 ✓ ✓✓ ✓ ✓ ✓ ✓

Total Stations 107

✶Data from the Wyatt light scattering photometer are available but the Hobi Lab Hydroscat II was not functioning cruise.
SIMBIOS Project Annual Report

31

Figure 4.1 Representative data taken from July 2, 2000 transect through Gulf of Maine coccolithophore bloom. Distance from

Yarmouth, NS is on the X axis for panels A-H. A) Surface temperature and salinity. The eastern Maine coastal current and

Jordan Basin water are indicated. B) Backscattering measured by the Wyatt light scattering photometer (514nm; based on 15

point volume scattering functions) and Hydroscat II (470 and 676nm; based on volume scattering at one angle). Water values

have been subtracted, thus bb values are from particulate and dissolved material. It can be seen that the Wyatt data show higher

frequency variability than the HSII, which is due to the smaller viewed volume (9ml vs 20L!). Moreover, both instruments

clearly show similar trends, but their relative values vary, possibly due to changing shape of the VSF (which is not taken into

consideration with the HSII, but is with the Wyatt). The coccolithophore bloom is plainly visible on either side of the stratified

Jordan Basin (60 and 170km). C) Acid-labile backscattering (bb’; measured with the Wyatt instrument), due to calcium

carbonate. Values in coastal current are not significantly different from zero (FlowCAM showed the algal assemblage

consisted of diatoms and dinoflagellates, with few coccolithophores. The ship moves 1.2 km during each 4’ acidification cycle.

Occasional negative bb’ values can result if phytoplankton bb values increase (due to horizontal variability) during an

acidification cycle (when bb values should decrease due to CaCO3 dissolution). In the coccolithophore bloom, calcite bb’

represented ~50% of the total backscattering (compare with panel C). D) Satlantic radiometer data for normalized waterleaving radiance compared with SeaWiFS values. At 60km, the ship passed through a fog bank, causing higher nLw values.

y = 0.9837x - 0.031

R2

 = 0.8218

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4

Ship (mW cm-2 um-1 sr-1;510nm)

SeaWiFS (mWcm-2um-1sr-1;510nm)

Includes only points where there is data from 

both sensors and shipboard sensor was not in fog

1:1

I

10

11

12

13

14

15

16

17

18

19

20

Temperature (C)

30

30.5

31

31.5

32

32.5

Salinity (ppt)

Temperature

Salinity A

Jordan Basin

Coastal Plume

0

0.1

0.2

0.3

0.4

0.5

Absorption (m-1;440nm)

F Coastal Plume

CDOM

Partic

0.0

0.2

0.4

0.6

0.8

1.0

Scattering (m-1;440nm)

G

CDOM

Partic

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

-300 -250 -200 -150 -100 -50 0

Distance (km)

Attenuation (m-1;440nm)

H

Cocco bloom

CDOM

Partic

-0.0005

0.0005

0.0015

0.0025

0.0035

 bb' (m-1;514nm)

C

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

-300 -250 -200 -150 -100 -50 0

Distance (km)

nLw (mW cm-2 um-1 sr-1;510nm)

Ship Measurement

SeaWiFS Measurement

Fog

Overpass

Portland, ME Yarmouth, NS

Sun Glint

Sun Glint

D

0.001

0.010

bb p+g (m-1)

bb 514nm bb 470nm

bb 676nm

B

Cocco bloom

0

0.5

1

1.5

2

2.5

Chl.a (mg m-3)

SeaWiFS

Ship

E
SIMBIOS Project Annual Report

32

Significantly increased nLw in the coccolithophore bloom was evident. E) Fluorescence- and SeaWiFS-derived (OC4)

chlorophyll concentrations. One can see that the OC4 algorithm generally overestimates chlorophyll by ~0.25μg l-1 in the Gulf

of Maine. F,G,H) IOP data on absorption, scattering, and attenuation taken with ac-9. Pure water values subtracted. The blue

region represents particle IOPs (>=0.2μm filterred), and the white region below represents the cDOM IOPs (< 0.2μm

diameter). One can see the increased cDOM a, b, and c in the Eastern Maine coastal current. Also apparent in Jordan Basin,

1/2 of the absorption, and 1/3 of the scattering is from cDOM. Scattering and absorption peaks clearly evident in the

coccolithophore bloom. I) SeaWiFS vs Ship measured nLw 440nm. Least-squares fit to the data has a slope which is not

significantly different from 1.0.

REFERENCES

Balch, W.M., Drapeau, D.T., Cucci, T.L., Vaillancourt, R.D.,

Ki4patrick, K.A. and Fritz, J.J., 1999: Optical

backscattering by calcifying algae--Separating the

contribution by particulate inorganic and organic carbon

fractions. Journal of Geophysical Research 104, 1541-

1558.

Bisagni, J.J., Gifford, D.J. and Ruhsam, C.M., 1996: The

spatial and temporal distribution of the Maine Coastal

Current during 1982. Continental Shelf Research, 1-24.

Femindez, E., Boyd, P., Holligan, P.M. and Harbour, D.S.,

1993: Production of organic and inorganic carbon within

a large scale coccolithophore bloom in the northeast

Atlantic Ocean. Marine Ecology Progress Series, 97,

271-285.

Haidar, A.T. and Thierstein, H.R., unpublished-a. Calcareous

phytoplankton standing stocks, fluxes and accumulation

in Holocene sediments off Ben-nuda (N. Atlantic). .

Haidar, A.T. and Thierstein, H.R., unpublished-b. Living

calcareotis phytoplankton dynamics off Bermuda (N.

Atlantic).

Hooker, S.B., Zibordi, G., Lazin, G. and McLean, S., 1999.

The SeaBOARR-98 Field Campaign, SeaWiFS

Postlaunch Technical Report Series, Vol. 3. NASA,

Greenbelt, MD, 40 pp.

JGOFS, 1996. Protocols for the Joint Global Ocean Flux

Study (JGOFS) core measurements. Report no. 19 of the

Joint Global Ocean Flux Study, Scientific Committee on

Oceanic Research, International Council of Scientific

Unions Intergovernmental Oceanographic Commission,

Bergen, Norway, 170 pp.

Maffione, R.A. and Dana, D.R., 1997. Instruments and

methods for measuring the backward-scattering

coefficient of ocean waters. Applied Optics , 36(24),

6057-6067.

Mueller, J.L. and Austin, R.W. (Eds.), 1995. Ocean optics

protocols for SeaWiFS validation, Revision. 1, SeaWiFS

Technical Report Series, Vol. 25. NASA Goddard Space

Flight Center, Greenbelt, MD, 67 pp.

O'Reilly, J.E. and Busch, D.A., 1984: Phytoplankton primary

production on the northwestern Atlantic shelf. Rapp. P. -

v. R6un. Cons. int. Explor. Mer, 183, 255-268.

O'Reilly, J.E., Evans-Zetlin, C. and Busch, D.A., 1987:

Primary production. In: Backus, R.H., Bourne, D.W.

(Eds.), Georges Bank, Massachusetts Institute of

Technology, Boston, pp. 220-233.

O'Reilly, J.E., Maritorena, S., Mitchell, B.G., Siegel, D.A.,

Carder, K.L., Garver, S.A., Kahru, M. and McClain, C.,

1998: Ocean color chlorophyll algorithms for SeaWiFS.

Journal of Geophysical Research, 103(Cl 1), 24937-

24953.

Sieracki, C.K., Sieracki, M.E. and Yentsch, C.S., 1998: An

imaging-in-flow system for automated analysis of marine

microplankton. Marine Ecology Progress Series, 168,

285-296.
SIMBIOS Project Annual Report

33

This research was supported by the

SIMBIOS NASA contract # 97268

PEER REVIEWED PUBLICATION

Balch, William M., David T. et al. 1999: Optical

backscattering by calcifying algae-Separating the

contribution by particulate inorganic and organic carbon

fractions J. Geophys. Res. 104: 1541-1558.

Gordon, Howard R., G. Chris Boynton, William M. Balch,

Stephen B. Groom, Derek S. Harbour , and Tim J. Smyth.

2000: Retrieval of Coccolithophore Calcite Concentration

from SeaWiFS Imagery. Geophysical Research Letters

(in press).

PRESENTATIONS

Balch, B., D.T. Drapeau, et al. 1998: Backscattering of Light

by Calcium Carbonae and Organic Matter in the Sea.

EOS, Trans AGU, 79:OS7.

Balch, W. M., Drapeau, D. T., Bowler, B., Ashe, A.,

Vaillancourt, R., Dunford, S., and Graziano, 1999:

Backscattering probability and calcite-dependent

backscattering in the Gulf of Maine. Proceedings of the

ASLO Meeting, Santa Fe.

Balch, W.B., H. R. Gordon, D. T. Drapeau, B. C. Bowler, A.

L. Ashe, J. I. Goes, and H. M. Gomes, 1999: PIC:POC

and the Remote Sensing of Biogenic Carbon in the Sea. In

press Trans. AGU.

Balch, W., D. Drapeau, B. Bowler, A. Ashe, J. Goes, E.

Scally. 2000. Anatomy of a coccolithophore bloom.

Presented at "Oceans from Space, Venice, 2000", Venice,

Italy, 9-13 Oct, 2000. European Communities, 2000. V.

Barale, J. F. R. Gower, and L. Alberotanza Convenors.
SIMBIOS Project Annual Report

34

Chapter 5

OCTS and SeaWiFS Bio-Optical Algorithm and Product

Validation and Intercomparison in U.S. Coastal Waters

Christopher W. Brown

NOAA/NESDIS, Camp Springs, Maryland

Ajit Subramaniam

University of Maryland, College Park, Maryland

Mary Culver

NOAA Coastal Services Center, Charleston, South Carolina

John C. Brock

USGS Center for Coastal Geology, St. Petersburg, Florida

5.1 INTRODUCTION

Monitoring the health of U.S. coastal waters is an

important goal of the National Oceanic and Atmospheric

Administration (NOAA). Satellite sensors are capable of

providing daily synoptic data of large expanses of the U.S.

coast. Ocean color sensor, in particular, can be used to

monitor the water quality of coastal waters on an operational

basis. To appraise the validity of satellite-derived

measurements, such as chlorophyll concentration, the biooptical algorithms used to derive them must be evaluated in

coastal environments. Towards this purpose, over 21 cruises

in diverse U.S. coastal waters have been conducted

(Subramaniam et al., 1997a, 1997b, 1997c, 1998, 1999,

2000a, 2000b; Culver et al. 1998; Kiambo et al. 1999). Of

these 21 cruises, 12 have been performed in conjunction with

and under the auspices of the NASA/SIMBIOS Project. The

primary goal of these cruises has been to obtain in-situ

measurements of downwelling irradiance, upwelling radiance,

and chlorophyll concentrations in order to evaluate bio-optical

algorithms that estimate chlorophyll concentration.

In this Technical Memorandum, we evaluate the ability of

five bio-optical algorithms, including the current SeaWiFS

algorithm, to estimate chlorophyll concentration in surface

waters of the South Atlantic Bight (SAB). The SAB consists

of a variety of environments including coastal and continental

shelf regimes, Gulf Stream waters, and the Sargasso Sea. The

biological and optical characteristics of the region is

complicated by temporal and spatial variability in

phytoplankton composition, primary productivity, and the

concentrations of colored dissolved organic matter (CDOM)

and suspended sediment. As such, the SAB is an ideal

location to test the robustness of algorithms for coastal use.

5.2 RESEARCH ACTIVITIES

Sampling Location and Collection Methods

Bio-optical measurements were collected at over 100

stations during nine cruises (Table 5.1) conducted in the South

Atlantic Bight in order to evaluate and validate the five

algorithms. The cruises were conducted from early spring to

late fall in optically diverse waters ranging from the extremely

shallow and turbid Pamlico Sound to the deep and clear

Sargasso Sea (Figure 5.1). Optical instruments measured

surface spectral downwelling irradiance, in-water spectral

downwelling irradiance, and upwelling radiance.

Although sampling strategies and instrument packages

varied between cruises, a Biospherical Instruments Profiling

Reflectance Radiometer (PRR) cage was typically deployed

off the stern of the vessel in conjunction with a reference

surface unit with matching channels. Surface bucket samples

were obtained for total suspended solids (TSS) concentration

and for chlorophyll analysis by fluorometric and HighPressure Liquid Chromatography (HPLC) techniques.

Detailed descriptions of instruments and other ancillary

measurements are presented in Subramaniam et al. (1997a,

1997b, 1997c, 1998, 1999, 2000a, 2000b) and Culver et al.

(1998).
SIMBIOS Project Annual Report

35

Table 5.1 Summary of cruise names, location, dates and sampling platforms.

Cruise Dates Number of

Stations

Location Vessel

FEB96LIT 22-23 Feb. 1996 7 Georgia Bight R/V Blue Fin

APR96BF 3-5 Apr. 1996 18 Georgia Bight R/V Blue Fin

APR96FER 22-25 Apr. 1996 16 Georgia Bight R/V Ferrel

MAY97OB 5 May 1997

8 May 1997

5

4

Onslow Bay, NC,

Pamlico Sound, NC

R/V Onslow Bay, R/V

Chipman

SEP97SAB 5-24 Sept. 1997 11 South Atlantic Bight R/V Cape Hatteras

NOV97SAR 4, 5 Nov. 1997 16 Saragasso Sea R/V Pametto

APR98SAB 5-27 April 1998 23 South Atlantic Bight R/V Cape Hatteras

NOV98SAB 27 Oct-23 Nov 1998 50 South Atlantic Bight R/V Cape Hatteras

FEB99SAB 27 Jan–24 Feb 1999 11 South Atlantic Bight R/V Cape Hatteras

Water Sample Analyses

Discrete water samples were collected following the PRR

cast from the sea surface using a bucket or a Niskin bottle and

filtered through glass fiber (GF/F) filters. The chlorophyll

samples were cold extracted in 10 ml of 90% acetone (10%

water) for 24 hours in the dark and the biomass was

determined fluorometrically with a Turner Designs

fluorometer as described in Subramaniam et al. (1998). The

TSS concentration was measured as described by Parsons et

al., (1984). For cruises FEB96LIT, APR96BF, APR96FER,

and NOV97SAR, chlorophyll a and other pigments were

determined as described in Subramaniam et al. (1997). For

the MAY97OB, SEP97SAB, APR98SAB and NOV98SAB

cruises, chlorophyll a and other pigments were determined as

described in Tester et al. (1995).

Quality Control

The PRR optical data were processed using the Bermuda

Bio-Optics Project (BBOP) processing software (Siegel et al.,

1995). All optical profiles were graphed and examined.

Profiles that exhibited evidence of surface perturbations, such

as ship shadow, and the effects of passing clouds were

excluded from further analysis. The in-situ downwelling

irradiance (Ed-) was propagated through the water-air

interface to Ed+ using a transmission loss of 4% (O’Reilly et

al., 1998). The in-situ upwelling radiance (Lu-) was

propagated through water-air interface to water-leaving

radiance (Lu+ or Lw) using a factor of 0.544 (O’Reilly et al.,

1998). The coefficient of variation (EsλErr) of the abovewater downwelling irradiance Es (λ) measured by the

reference sensor mounted on the ship was calculated as the

ratio of the standard deviation to the mean of the Es(λ)

measurements for the duration of the PRR600 profile. Es

from profiles where EsλErr was greater than 10% (indicating

either passing clouds or large ship roll) was not used in

calculating remote sensing reflectance. The difference (dsλ)

between the measured downwelling irradiance (Es) and the

calculated downwelling irradiance (Ed+) was calculated and

profiles with dsλ greater than 50% were excluded from

analysis. Several other stations that possessed peculiar spectra

were also eliminated. The remote sensing reflectance Rrs (λ)

and normalized water-leaving radiances nLw (λ) were

calculated as

(0 , ) ( ) 0.544* ( )

u

rs

s

L R

E

λ λ

λ

−

=

and

0 () * () rs nLw F R λ λ =

Algorithm Evaluation

Optical profiles from a total of 88 stations were used to

evaluate the five bio-optical algorithms developed to estimate

surface chlorophyll concentration from satellite ocean color

observations. These algorithms included the current SeaWiFS

algorithm (OC4v4; O’Reilly et al., 2000), the previous

SeaWiFS algorithm and its improvement (OC2v4 and OC2v2;

O’Reilly et al., 1998), an algorithm proposed for the

Southeastern United States (OCse; Stumpf et al., 2000), and a

semi-analytical algorithm based on Garver and Siegel’s

inverse model (UCSB; Garver and Siegel, 1997; Maritorena et

al., 2000). OCse is an empirical algorithm developed using

data collected from highly absorbing waters of the Gulf of

Mexico. The semi-analytical algorithm (UCSB) was selected

because it explicitly estimates backscatter and CDOM

absorption, in addition to chlorophyll concentration, and had

the potential of reducing the error attributed to high

concentrations of suspended sediments and riverine

contribution of CDOM found in the South Atlantic Bight. The

formulations used for each algorithm to calculate chlorophyll

concentration were as follows:

( ) 2 3 4 0.336 3.067 1.930 0.649 1.532 ( 4 4) 10 X X X X OC v Chl − + + − = ,

where 













 = > >

(555)

(510)

(555)

(490)

(555)

(443) log

rs

rs

rs

rs

rs

rs

R

R

R

R

R

R

X .
SIMBIOS Project Annual Report

36

( ) 2 3 0.2974 2.2429 0.8358 0.0077 OC2v2Chl 0.0929 10 − X + X − X = − + ,

where 













 = (555)

(490) log

rs

rs

R

R

X .

( ) 2 3 0.319 2.336 0.879 0.135 OC2v4Chl 0.071 10 − X + X − X = − + ,

where 













 = (555)

(490) log

rs

rs

R

R

X .

















−

= (555)

(490) 2.5 log(

10 rs

rs

R

R

OCseChl .

OCse is valid only in (coastal) regions of high chlorophyll

concentration (Stumpf et al., 2000). For regions possessing

low chlorophyll concentrations, the OC4v4 algorithm was

applied. For regions containing moderate chlorophyll

concentrations, a log-transformed weighting was used to shift

from OC4v4 (low chlorophyll waters) to OCse (high

chlorophyll concentrations). The transform was applied based

on the following criteria:

a) OCseChl < 0.1 mg/m3

, chlorophyll = OC4v4Chl

(OC4v4Chl ~ 0.2 mg/m3)

b) mg/m3 < OCseChl < 0.5 mg/m3

,

chlorophyll=

log( ) log(0.1) log(0.5) log( ) log( )* log( 4 4)* log(0.5) log(0.1) log(0.5) log(0.1) 10

OCse OCse OCse OC v

OCseChl

  − −   +

  − − =

c) OCseChl > 0.5 mg/m3, chlorophyll = OCseChl

The logarithmic weighting was used as the OC4v4 and

OCse algorithms are both in terms of log(Chl). The results

remove the bias found between OC4v4 and the measured

chlorophyll. A simple linear regression analysis between

measured chlorophyll and algorithm chlorophyll in log space

was performed to evaluate the algorithms. Only

fluorometrically determined chlorophyll concentrations (ChlF)

were employed in this evaluation. Typical measures of

goodness-of-fit between in-situ chlorophyll concentrations and

modeled retrievals, such as the coefficient of determiniation,

r

2

, were calculated and examined. In addition, an Algorithm

Performance Index (API) was calculated as the log of the ratio

of algorithm derived chlorophyll to measured chlorophyll.

Consequently, an API value of 0 indicates that the algorithm

predicted the measured chlorophyll concentration, a negative

value indicates the algorithm underestimated chlorophyll, and

a positive indicates the algorithm overestimated chlorophyll.

5.3 RESEARCH RESULT

Bottle Samples

In-situ chlorophyll (ChlF) values ranged from 0.16 to 5.20

μg/L with mean value of 1.51 μg/L and a median value of

1.03 μg/L. While many of the high chlorophyll stations lay

along the coast and the low chlorophyll (0-1 μg/L) stations

were situated along the outer shelf, no distinct spatial pattern

was discernible in the in-situ chlorophyll concentrations

(Figure 5.2). The absence of any obvious pattern in

chlorophyll concentration is likely due to the temporal span

over which the data were collected and the dynamic nature of

phytoplankton biomass in the SAB. For example, surface

chlorophyll concentration at a station located at the shelf break

in September 1997 was 0.33 μg/L while a station occupied at

the same position in November 1998 was 1.11 μg/L. This

large variation could be attributable to interactions of the Gulf

Stream with the shelf waters (McClain et al. 1984).

Algorithm Validation and Evaluation

Comparisons of measured and algorithm-derived

estimates of chlorophyll concentration are illustrated in

Figures 5.3 and 5.4. Figure 5.3 illustrates the frequency

distribution of both measured and algorithm-derived values of

chlorophyll concentration (ChlF) observed during our cruises

in the South Atlantic Bight. In general, OC2v2 performed

well at lower chlorophyll concentrations (up to 0.3 mg m-3).

As expected, OCse performed well in the high chlorophyll

range (> 0.5 mg chl m-3), with the shape of its cumulative

frequency similar to that of in-situ chlorophyll concentration

at values of 1 mg chl m-3 and greater. The overestimation in

the 0.3 to 0.5 mg m-3 range is potentially due to the

logarithmic weighting over this concentration interval. UCSB

also exhibits roughly the same cumulative frequency shape as

in-situ chlorophyll at higher chlorophyll concentrations,

though it is less sigmoidal. Analysis of OC2v4, the

“improved” version of OC2v2, performed substantially worse

than its predecessor and was not presented.

Results of least-squares regression analysis are presented

in Table 5.2. Of the five algorithms evaluated for the SAB,

UCSB possessed the slope closest to 1.00 (slope = 1.036). All

algorithms except OC4v4 displayed similar intercepts with a

mean of 0.27 (n=4). The intercept of OC4v4 was almost twice

as great (0.5). OC4v4, however, received the highest

coefficient of determination (r2

 = 0.72), while UCSB received

the lowest (r2

 = 0.52). The overall performance of UCSB was

degraded by a few model retrievals that severely

underestimated actual chlorophyll concentration (Fig. 5.4).

Table 5.2 Results of regression analysis for each algorithm.

Algorithm OC2v2 OC2v4 OC4v4 OCse UCSB

Slope 1.361 2.660 0.931 0.699 1.036

Intercept 0.288 0.299 0.503 0.236 0.269

r

2 0.693 0.644 0.721 0.695 0.523

Examining the Algorithm Performance Index (API) of the

algorithms for all data indicated that OC2v2, OC2v4, and

OC4v4 overestimated actual chlorophyll concentrations to
SIMBIOS Project Annual Report

37

varying degrees, while OCse and UCSB underestimated them

(Table 5.3). Average API values for UCSB suggest it

performed very well. This result, however, was fortuitous.

Close examination revealed that the “mean” value was

achieved by averaging the overestimates and underestimates

of individual measurements.

Dividing the data collected in “spring” (February-May)

and “non-spring” (June-January) months indicated a seasonal

component to algorithm performance (Table 5.3). Algorithms

generally performed better, i.e. API approached 0, during the

non-spring months. During the spring, all algorithms

overestimated measured chlorophyll concentrations (Table

5.3). This overestimation is likely to result from increases in

CDOM concentration (and absorption) caused by elevated

river discharge into the SAB during the spring. The spatial

distribution and number of stations in the spring and nonspring periods were similar, eliminating geographic bias.

5.4 CONCLUSIONS

We evaluated the performance of five chlorophyll a

algorithm in the South Atlantic Bight by comparing

radiometrically-derived chlorophyll concentrations and in-situ

chlorophyll concentrations. The results indicate that

biogeographical provinces alone do not improve algorithm

performance in the SAB. Seasonal variation must be taken

into account. The high variability observed in spring is likely

due to the presence of high concentrations of CDOM in shelf

waters. Consequently, we expected that an algorithm that

accounts for CDOM is necessary to accurately estimate

chlorophyll concentration in the South Atlantic Bight. It is

therefore surprising that UCSB, the algorithm that explicitly

solves for CDOM absorption, does not perform well in the

SAB. It’s poor performance may be due to several reasons.

One, it requires accurate measurements from 412 to 555 nm.

In waters containing high concentrations of CDOM, in which

the signal at 412 and 443 nm are very low, small errors in the

propagation of Lu through the surface may generate large

errors in the estimated chlorophyll concentration. Two, the

algorithm is driven by a statistical tuning that is based on a

large number of pixels that may not be appropriately analyzed

by individual measurements. OCse, the regional algorithm,

worked reasonably well in the SAB and may be improved by

changing the structure of its log-transformed weighting

function over the chlorophyll concentration range of 0.3 - 0.5

mg m-3.

ACKNOWLEDGEMENTS

In addition to funding provided by the NASA/SIMBIOS

Project, this work was accomplished in partnership with many

agencies and institutions, including the National Marine

Fisheries Service Southeast Fisheries Science Center, Grice

Marine Laboratory of the College of Charleston, Skidaway

Institute of Oceanography. We sincerely thank Dr. P. Tester

for the use of her data and her invaluable participation in all

aspects of this project.

Table 5.3 Mean chl. concentration (ChlF) & Algorithm Performance Index (API) for each algorithm by cruise and season.

Cruise ChlF API

OC2v2

API

OC2v4

API

OC4v4

API

OCse

API

UCSB

SEP97SAB 2.048 0.028 0.108 0.022 -0.176 0.039

MAY97OB 1.977 0.133 0.219 0.091 -0.049 -0.043

NOV97SAR 0.227 0.113 0.054 0.165 0.084 -0.040

NOV98SAB 1.042 -0.090 -0.097 -0.041 -0.227 -0.134

Non-Spring 1.314 -0.002 0.019 0.020 -0.129 -0.077

APR98SAB 1.619 0.348 0.507 0.278 0.077 0.041

APR96FER 1.943 0.218 0.349 0.151 -0.049 -0.008

FEB96LIT 1.134 0.531 0.622 0.527 0.249 0.552

FEB99SAB 0.784 0.129 0.162 0.073 -0.016 0.103

Spring 1.533 0.276 0.397 0.214 0.034 0.079

Total 1.421 0.132 0.202 0.114 -0.062 -0.003

REFERENCES

Culver, M.E., A. Subramaniam, J.C. Brock, M.E. Geesey,

G.R. DiTullio, D. Jones, 1998: NOAA CSC/CRS Cruise

NOV97SAR: Sargasso Sea Cruise. CSC Technical

Report CSC/9-98/002. U.S. Department of Commerce,

National Oceanic and Atmospheric Administration, Coastal

Services Center, Charleston, SC.

Garver, S. A. and D. A. Siegel, 1997: Inherent optical property

in version of ocean color spectra and its biogeochemical

interpretation. 1. Time series from the Sargasso Sea.
SIMBIOS Project Annual Report

38

Journal of Geophysical Research 102(C8), 18,607-

18,625.

Kiambo, R. W., M. E. Culver, P. A. Tester, R.P.Stumpf, A.

Subramaniam, J. C. Brock and D. Eslinger, 1999: NOAA

Cruise APR98SAB: South Atlantic Bight Cruise. CSC

Technical Report. CSC/1-99/001. U.S. Department of

Commerce, National Oceanic and Atmospheric

Administration, Coastal Services Center, Charleston, SC.

McClain, C.R., L.J. Pietrafesa, and J.A. Yoder, 1984:

Observations of Gulf Stream-induced and wind-driven

upwelling in the Georgia Bight using ocean color and

infrared imagery. Journal of Geophysical Research 89:

3705-3723.

Maritorena, S., Siegel, D.A., Peterson, A.R. & LorenziKayser, M. 2000: Tuning of a pseudo-analytical ocean

color algorithm for studies at global scales. Presented at

the 2000 AGU Ocean Sciences Meeting (OS12M-05),

San Antonio TX, 2000

O'Reilly, J. E., S. Maritorena, B. G. Mitchell, D. A. Siegel, K.

L. Carder, S. A. Garver, M. Kahru and C. McClain, 1998:

Ocean color chlorophyll algorithms for SeaWiFS.”

Journal of Geophysical Research 103 (C11): 24937-

24953.

O'Reilly, J. E., and et al. , 2000: Ocean color chlorophyll a

algorithms for SeaWiFS, OC2 and OC4: Version 4.

SeaWiFS Postlaunch Calibration and Validation

Analyses, part 3. NASA SeaWiFS technical report series.

pp. 8 – 22.

Parsons, T. R., Y. Maita and C. M. Lalli , 1984: A Manual

For Chemical And Biological Methods For Seawater

Analysis, Pergamon Press.

Siegel, D.A., M.C. O’Brien, J.C. Sorensen, D.A. Konnoff and

E. Fields, 1995: BBOP Data Processing and Sampling

Procedures. Vol: 19, Institute for Computational Earth

System Science, UC Santa Barbara, Santa Barbara, CA,

23pp.

Stumpf, R. P., R. A. Arnone, R. W. Gould, P. Martinolich, V.

Ransibrahmanakul, P. A. Tester, R. G. Steward, A.

Subramaniam, M. E. Culver and J. R. Pennock, 2000:

SeaWiFS Ocean Color Data For U.S. Southeast Coastal

Waters. Sixth International Conference on Remote

Sensing for Marine and Coastal Environments,

Charleston, SC, Veridian ERIM International.

Subramaniam, A., E.M. Armstrong, K.J. Waters, J.C. Brock,

P.A. Tester, E.Haugen. 1997a: NOAA CSC/CRS Cruise

MAR97OCC: OCTS Calibration Cruise. CSC Technical

Report CSC/5-97/001. U.S. Department of Commerce,

National Oceanic and Atmospheric Administration,

Coastal Services Center, Charleston, SC.

Subramaniam, A., E.M. Armstrong, K.J. Waters, J.C. Brock,

A.W. Meredith, R.O. Ranheim. 1997b: NOAA CSC/CRS

Cruise MAY96NY: New Bight Apex Cruise. CSC

Technical Report CSC/6-97/001. U.S. Department of

Commerce, National Oceanic and Atmospheric

Administration, Coastal Services Center, Charleston, SC.

Subramaniam, A., K.J. Waters, A.W. Meredith, E.M.

Armstrong, R.M Bohne, W.G. Keull, J.R. Nelson, G.R.

DiTullio, J.C. Brock. 1997c: NOAA CSC/CRS Cruise

APR96FER: Gray’s Reef Cruise. CSC Technical Report

CSC/7-97/001. U.S. Department of Commerce, National

Oceanic and Atmospheric Administration, Coastal

Services Center, Charleston, SC.

Subramaniam, A., J.C. Brock, P.A. Tester, E. Haugen, R.P.

Stumpf, 1998: NOAA NMFS Cruise MAY97OB: Onslow

Bay and Pamlico Sound Cruise. CSC Technical Report

CSC/2-98/001. U.S. Department of Commerce, National

Oceanic and Atmospheric Administration, Coastal

Services Center, Charleston, SC.

Subramaniam, A., M. Culver, D. Phinney, D. Phinney, J.

Brown, B. Schieber, J. Brock, D. Eslinger, and C. Brown.,

1999: NOAA Cruise JUL98NAN: Nantucket Shoals

Cruise. NOAA CSC Technical Report, 99043-PUB. U.S.

Department of Commerce, National Oceanic and

Atmospheric Administration, Coastal Services Center,

Charleston, SC.

Subramaniam, A., N. Blough, L. Harding, M. E. Geesey, C.

W. Brown and M. E. Culver, 2000: NOAA Cruise

OCT99MAB: Mid Atlantic Bight Cruise. Technical

Report. CSC/20016-PUB. U.S. Department of Commerce,

National Oceanic and Atmospheric Administration,

Coastal Services Center, Charleston, SC.

Subramaniam, A., M. E. Culver and M. E. Geesey, 2000:

NOAA Cruise APR00FWS: West Florida Shelf Cruise.

CSC Technical Report. CSC/20018-PUB. U.S.

Department of Commerce, National Oceanic and

Atmospheric Administration, Coastal Services Center,

Charleston, SC.

Tester, P.A., M.E. Geesey, C. Guo. H.W. Paerl and D.F.

Millie, 1995: Evaluating phytoplankton dynamics in the

Newport River estuary (North Carolina, USA) by HPLCderived pigment profiles. Mar. Ecol. Prog. Ser. 124, 237-

245.
SIMBIOS Project Annual Report

39

Figure 5.1 Location of stations.

Figure 5.2 Spatial pattern of measured chlorophyll concentration in the South Atlantic Bight in this study.
SIMBIOS Project Annual Report

40

0

5

10

15

20

25

30

-1 -0.7 -0.5 -0.3 0 0.3 0.5 0.7 1 More

Log(ChlF)

Frequency

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Measured ChlF OC4v4 OC2v2 OCse UCSB

Measured Cumulative OC4v4 Cumulative OC2v2 Cumulative Ocse Cumulative UCSB Cumulative

Figure 5.3 Histogram of measured and algorithm-derived chlorophyll concentrations.

0.001

0.01

0.1

1

10

100

0.1 1 10

Fluorometric Chl (mg/m^3)

Algorithm Chl (mg/m^3)

Chl(UCSB)

Chla(OC4)

Chla(OC2v4)

Chla(OC2v2)

Chl(OCse)

Figure 5.4 Scatter plot of measured and algorithm-derived chlorophyll concentration.
SIMBIOS Project Annual Report

41

This research was supported by the

SIMBIOS NASA interagency agreement # 97879

OTHER PUBLICATIONS

Subramaniam, A., E.M. Armstrong et al., 1997a: NOAA

CSC/CRS Cruise MAR97OCC: OCTS Calibration

Cruise. CSC Technical Report CSC/5-97/001.

Subramaniam, A., E.M. Armstrong et al.,1997b: NOAA

CSC/CRS Cruise MAY96NY: New York Bight Apex

Cruise. CSC Technical Report CSC/6-97/001.

Subramaniam, A., K.J. Waters et al.,1997c: NOAA CSC/CRS

Cruise APR96FER: Gray’s Reef Cruise. CSC Technical

Report CSC/7-97/001.

Subramaniam, A., J.C. Brock et al., 1998: NOAA NMFS

Cruise MAY97OB: Onslow Bay and Pamlico Sound

Cruise. CSC Technical Report CSC/2-98/001.

Subramaniam, A., C.W. Brown et al., 1999: Comparison of

algorithms to determine chlorophyll concentration from

satellite coean color in three regions: South Atlantic

Bight. CSC Technical Report 99032-PUB.

PRESENTATIONS

Geesey, M. , M. Culver, A. Subramaniam, G. DiTullio, and J.

Brock. 1998: Optical and biological variability in the

South Atlantic Bight. Conference for Collaborative

Research Activities in the South Atlantic Bight.

Culver, M. E., K. J. Waters, A. Subramaniam, D. A. Phinney,

W. M. Balch, and D. T. Drapeau. 1998: Closure of

inherent and apparent optical properties at a coastal

station in the Gulf of Maine. Ocean Optics XIV.

Subramaniam, A. M.E. Culver, P.A. Tester, C.W. Brown, J.C.

Brock. 1998: An Evaluation of the OC2 SeaWiFS

Algorithm Performance in the South Atlantic Bight.

Ocean Optics XIV.

Eslinger, D. L., M. E. Culver, D. Aurin, R. Swift, and F. Hoge.

1999: A Comparison of Active and Passive Ocean Color

in Southeastern U.S. Coastal Waters. Estuarine Research

Federation.
SIMBIOS Project Annual Report

42

Chapter 6

Validation of Ocean Color Satellite Data Products in Under

Sampled Marine Areas

Ajit Subramaniam

University of Maryland, College Park, Maryland

Raleigh R. Hood

University of Maryland Center for environmental Science, Maryland

Christopher W.Brown

NOAA/NESDIS, Camp Spring, Maryland

Edward J. Carpenter

SUNY at Stony Brook, New York

Douglas G. Capone

University of Southern California, California

6.1 INTRODUCTION

The planktonic marine cyanobacterium, Trichodesmium

sp., is broadly distributed throughout the oligotrophic marine

tropical and sub-tropical oceans (Capone et al., 1997).

Trichodesmium, which typically occurs in macroscopic

bundles or colonies, is noteworthy for its ability to form large

surface aggregations and to fix dinitrogen gas. The latter is

important because primary production supported by N2

fixation can result in a net export of carbon from the surface

waters to deep ocean and may therefore play a significant role

in the global carbon cycle (Hood et al. 2000). However,

information on the distribution and density of Trichodesmium

from shipboard measurements through the oligotrophic oceans

is very sparse. Such estimates are required to quantitatively

estimate total global rates of N2 fixation. As a result current

global rate estimates are highly uncertain (Hood et al. 2000).

Thus in order to understand the broader biogeochemical

importance of Trichodesmium and N2 fixation in the oceans,

we need better methods to estimate the global temporal and

spatial variability of this organism. One approach that holds

great promise is satellite remote sensing. Satellite ocean color

sensors are ideal instruments for estimating global

phytoplankton biomass, especially that due to episodic

blooms, because they provide relatively high frequency

synoptic information over large areas. Trichodesmium has a

combination of specific ultrastructural and biochemical

features that lend themselves to identification of this organism

by remote sensing. Specifically, these features are high

backscatter due to the presence of gas vesicles, and absorption

and fluorescence of phycoerythrin. The resulting optical

signature is relatively unique and should be detectable with

satellite ocean color sensors such as SeaWiFS (Subramaniam

et al. 1999b).

Surface aggregations of Trichodesmium have been noted

in the Great Barrier Reef and northern Australian waters as

early as 1770 (Furnas, 1992). Marshall (1933) noted that

surface manifestations were highly aggregated and of short

duration in his one year long study around the Low Isles, north

of Cairns. Revelante and Gilmartin (1982) did a two year long

study and found up to 40,000 trichomes/L off Townsville

[assuming 200 trichomes/colony and 50 ng Chl/colony,

(Carpenter, 1983)] - this gives a Trichodesmium specific

chlorophyll concentration of 10 mg Chl/m3

. Kuchler and Jupp

(1988) showed a dramatic space shuttle astronaut hand held

camera photograph of a 150-km long Trichodesmium bloom in

the Capricorn Channel in the southern end of the Great Barrier

Reef. More recently, Glibert et al. (2000) reported a

Trichodesmium bloom off Heron Island that ranged from 200

colonies/L to 5000 colonies/L (10-250 mg Chl/m3

).

Trichodesmium is also known to routinely occur along the

southeastern coast of the United States off Florida, Georgia

and South Carolina (Hulbert, 1967; Marshall, 1971). Dunstan

and Hosford (1977) found that Trichodesmium was endemic to

the continental shelf in the South Atlantic Bight and not

necessarily contributed by the Gulf Stream or connected to
SIMBIOS Project Annual Report

43

[ ] * * 0.02( 412)

* * 0.62

( ) ( ) ( ) 0.05

)] 550 ( ) ( ) ( ) 0.30 [0.02(0.5 0.25log )(

( ) 0.083 − − + + +





 



 + + + −

= λ λ λ λ

λ

λ λ λ

λ

a a C a C e

b b C b C C C C

R

w p p T T

bw bp p bT T

rs (6.1)

sub-surface Gulf Stream intrusions. They found

Trichodesmium all through the year in at least 36% of the

stations (in December) to 76% of the stations (in July). The

densest concentrations were usually at midshelf stations

between the 20m and 200m isobaths. In July 1974, they

encountered a shelf-wide bloom with 2000 (0.5 mg Chl/m3

) to

5600 (1.4 mg Chl/m3) trichomes/L and a maximum of 36,000

trichomes/L (9 mg Chl/m3 Trichodesmium specific

chlorophyll).

It is apparent that these two marine areas, Capricorn

Channel in the Southern Great Barrier Reef and the South

Atlantic Bight, routinely experience high concentrations of

Trichodesmium. We present here the development of a

classification algorithm for Trichodesmium, based on a bloom

observed by the SeaWiFS satellite in the Capricorn Channel

and apply that algorithm to map another bloom encountered

during a cruise in October 1998 in the South Atlantic Bight.

6.2 RESEARCH ACTIVITIES

The optical model used here (Eq. 6.1) parameterizes the

expression for remote sensing reflectance (Rrs) presented by

Kirk (1994). The parameterization is similar to that described

by Subramaniam 1999(b), the major differences being that the

present model is parameterized only for five wavelengths and

uses coefficients relevant to the South Atlantic Bight.

To compute the reflectance spectra for varying

concentrations of only Trichodesmium, the chlorophyll

concentration of “other phytoplankton” was set to 0, and

conversely to compute the reflectance spectra for varying

concentrations of only “other phytoplankton”, the chlorophyll

concentration of Trichodesmium was set to 0. To study the

effect of mixed populations, the reflectance spectra of a

varying percentage of Trichodesmium to other phytoplankton

for a total chlorophyll concentration of 1 mg Chl/m3

 was also

computed.

Image Processing and Classification Development

A bright feature was noticed in a SeaWiFS true color

image of the Capricorn Channel of July 16th 1998 by Norman

Kuring at the SeaWiFS Project office and brought to our

attention. We determined that Trichodesmium blooms were

noted anecdotally from Heron Island during this period

although no samples were taken (P. Bird personal

communication). Weather observations taken there showed

that the winds were light on the 14th (5-7 knots) and 15th (8-10

knots), and in general, conditions were favorable for a

Trichodesmium bloom. The putative Trichodesmium bloom

was seen as a bright feature (lighter blue) in the middle of the

Capricorn Channel where the water depths are between 50 and

80 m. The shape of this bloom and its location is remarkably

similar to the shape and location of a Trichodesmium bloom

photographed from the space shuttle described by Kuchler and

Jupp (1988).

High Resolution Picture Transmission (HRPT) SeaWiFS

images for the Capricorn Channel and the South Atlantic

Bight were obtained from the Goddard Distributed Active

Archive Center and were processed from raw level 1b to level

2 using the l2gen procedure of SeaDAS (version 3.3, Fu et

al.1998). Level 2 products included normalized water leaving

radiance (nLw) at 412, 443, 490, 510, and 555 nm

corresponding to bands 1 to 5 of the SeaWiFS sensor and

chlorophyll concentration as calculated by the OC2 algorithm

(O'Reilly et al 1998). Transects were taken through the bloom

using the Rline procedure of SeaDAS to extract the

chlorophyll concentration and the nLw values for the five

bands.

The spectral characteristics along the transect showed that

nLw443, nLw490, and nLw510 were relatively high inside the

bloom (nLws > 1.6) with nLw490 being the highest, but

rapidly fell outside the bloom (nLw < 1). Our optical model

results described in detail in section 3.1 below, predicts that

nLw 443, nLw490, nLw510 and nLw555 should be higher for

Trichodesmium than for other phytoplankton at any

chlorophyll concentration due to the high backscatter from gas

vesicles found in Trichodesmium. The model also predicts

that for Trichodesmium specific chlorophyll concentrations of

between 0.5 and 1.5 mg Chl/m3

, nLw490 should have the

highest magnitude. The magnitude of nLw510 was greater

than that of nLw443 inside the transect of the bloom as

predicted by the model. After analysis of various

combinations of nLw443, nLw490, nLw555 from inside and

outside the bloom, we found that the ratio of the difference

between nLw490 and nLw443 to nLw490 and nLw555 best

delineated the Trichodesmium bloom from the rest of the

phytoplankton. The model results show that this "shape

criteria" is not only valid for lower concentrations of

Trichodesmium (<2 mg Chl/m3

), but is also valid for higher

concentrations (2-4 mg Chl/m3

) of other phytoplankton as

well. However, other phytoplankton did not have high nLws

at these concentrations.

Based upon these model predictions and SeaWiFS

observations, we developed a set of classification criteria to

identify the presence of moderate concentrations (0.8-2 mg

Chl/m3

) of Trichodesmium using SeaWiFS derived normalized

water leaving radiances. A pixel is flagged as dominated by

Trichodesmium if the following criteria are satisfied:
SIMBIOS Project Annual Report

44

• nLw490 > 1.3 - absolute magnitude criteria

• nLw510 > nLw443 - relative magnitude criteria

• 0.4 > (nLw490 - nLw443)/(nLw490-nLw555) > 0.6 -

shape criteria

Subramaniam et al. (1999a) showed that standard ocean

color chlorophyll algorithms would underestimate

Trichodesmium specific chlorophyll by a factor of at least 4

due to self-shading by the colonies (the "secondary packaging

effect"). Therefore the chlorophyll concentrations derived by

OC2 at pixels flagged as Trichodesmium dominated were

multiplied by 4 to retrieve Trichodesmium specific chlorophyll

concentrations.

On application of this classification to SeaWiFS data from

various regions, we found that high water leaving radiance due

to bottom reflectance from shallow waters and certain

combinations of sediments and colored dissolved organic

matter mimic the reflectance pattern of a Trichodesmium

bloom. To avoid this, a 30m bathymetric mask was added to

the classification scheme. We found that the spectral

reflectance signature of Phaeocystis is very similar to

Trichodesmium in magnitude and shape. However,

Phaeocystis is predominantly a cold water species whereas

Trichodesmium normally does not bloom in waters colder than

23°C and can be effectively screened by using a temperature

criterion of 25°C. In order to avoid potential false positives

from resuspended sediments and bright diatom blooms, we

also exclude pixels where the wind speed is greater than 8 m/s.

6.3 RESEARCH RESULTS

Model results

Subramaniam et al (1999b) showed that Trichodesmium

could not be distinguished from other phytoplankton at

concentrations less than about 1.0 mg Chl/m3

. Here we model

the normalized water leaving radiance (nLw) at 412, 443, 490,

510 and 555 nm corresponding to bands 1, 2, 3, 4, and 5 of

SeaWiFS for Trichodesmium and other phytoplankton for

chlorophyll concentrations ranging from 0.5-10 mg Chl/m3

.

As expected, the model predicts that the nLw spectrum of

Trichodesmium should be different from that of other

phytoplankton, especially at high chlorophyll concentrations

(Figure 6.1a, 6.1b). Even at 0.5 mg Chl/m3

, while the spectra

of nLw for Trichodesmium and other phytoplankton are

similar, the model predicts that the magnitude of nLw490,

nLw510 and nLw555 should be higher for Trichodesmium due

to the high backscatter contribution of its gas vacuoles. The

model also predicts that the magnitude of nLw412 for

Trichodesmium should always be lower than that of other

phytoplankton due its higher chlorophyll specific absorption

as described by Subramaniam et al. 1999a. At Trichodesmium

chlorophyll specific concentrations greater than 0.5 mg

Chl/m3

, the model predicts that the magnitude of nLw510 will

be greater than nLw443.

However, comparison of the model predicted nLw spectra

for pure Trichodesmium populations to nLw spectra derived

from SeaWiFS imagery give ambiguous results (Figure 1c). A

global search of the SeaWiFS imagery revealed very few

pixels with the spectrum that closely resembled the spectrum

that is generated by the model at high Trichodesmium

concentrations, i.e. we could not find any dense

Trichodesmium blooms in the ocean with the model predicted

spectrum. This may be because the modeled spectra are for

pure populations of Trichodesmium, whereas, in the field, one

is more likely to find mixed populations. In order to examine

the impact of mixed populations on the predicted spectra, the

model was run for increasing proportions of other

phytoplankton to Trichodesmium from 10% other

phytoplankton to 90% other phytoplankton for a total

chlorophyll concentration of 1 mg Chl/m3

 (Figure 6.1d). As

anticipated, the reflectance at 490nm is highest for 10% other

phytoplankton due to the high backscatter at this wavelength

from Trichodesmium and lowest for 90% other phytoplankton

where the influence of Trichodesmium on the spectrum is

small. The spectral shape appears to flatten as the amount of

Trichodesmium is reduced. The spectral shape and magnitude

of the normalized water leaving radiance spectrum from a

pixel in the middle of the Capricorn Channel bloom is very

close to that of a mixture of 40% Trichodesmium and 60%

other phytoplankton (Figure 6.1d) showing that the SeaWiFS

observed spectra are that of a mixed population. Thus, the

model-predicted spectra for mixed populations of

Trichodesmium and other phytoplankton can be found in

SeaWiFS imagery. In fact, even in cases of very dense, nearly

monospecific blooms, the satellite will likely always see a

mixture of Trichodesmium and other phytoplankton species

because a SeaWiFS pixel is 1 km2

 while Trichodesmium

blooms tend to occur in linear patches or striated patterns with

width scales of 10s of meters. Thus, even the densest blooms

extending 100s of km may never entirely fill a pixel and will

therefore always include other optical influences such as other

phytoplankton.

Mapping of Capricorn Channel Bloom.

The OC2 derived chlorophyll image (Figure 2a at

http://www.usc.edu/dept/LAS/biosci/tricho/manuscripts/)

shows four patches of apparently high chlorophyll

concentrations (~0.7 mg chl/m3

). The apparently high

chlorophyll concentration over the Great Barrier Reef (3) is

probably an artifact of bottom reflection. While the high

chlorophyll along the coast around Broad Sound (4) and off

Frasier Island (6) maybe real, bottom reflection and sediments

also probably contaminate those values. However, the high

chlorophyll patch in the middle of Capricorn Channel has

optical properties that are consistent with the presence of a

Trichodesmium bloom. The adjacent Coral Sea region outside

the Great Barrier Reef has chlorophyll values of 0.1 mg

Chl/m3

. The apparent Trichodesmium bloom as mapped by

the classification scheme (Figure 2b at
SIMBIOS Project Annual Report

45

http://www.usc.edu/dept/LAS/biosci/tricho/manuscripts/)

shows up as two patches. One is a larger patch in the middle

of the Capricorn Channel; there is also a smaller patch off

Cape Manifold. It is very likely that the classification rules

are very conservative and do not map the entire

Trichodesmium bloom. The scheme is a binary flag, where a

pixel either satisfies a set criteria or fails, it cannot reproduce

the gradual tail off that would likely be found in nature.

The normalized water leaving radiance values were

extracted for each band along a transect through the bright

feature (transect shown in Figure 2a, b at

http://www.usc.edu/dept/LAS/biosci/tricho/manuscripts/).

The spectral characteristics along the transect between 0 and

40 km are that of Trichodesmium (Figure 3 at

http://www.usc.edu/dept/LAS/biosci/tricho/manuscripts/), i.e.

nLw443, nLw490, and nLw510 are relatively high, (nLw >

1.6, 0-40 km). Beyond 40 km, the radiances decline rapidly

(nLw < 1, 65-120 km). Between 40 and 60 km, the transect

appears to pass through the edge of another filament of water

dominated by Trichodesmium (at 51 km). The shape criteria

(band3 - band2)/(band3-band5) is satisfied in the region 0 to

40 km and in the filament at 51 km but not elsewhere. But the

chlorophyll values calculated by OC2 remains relatively high

through most of the transect at about 0.6 mg Chl/m3

 inside the

apparent Trichodesmium bloom and 0.4 mg Chl/m3

 along the

rest of the transect. Thus, although Trichodesmium appears to

dominate the optical properties in the center of the bloom,

these waters are rich in other phytoplankton as well. As

discussed above, the spectral shape and magnitude of a pixel

from the center of the bloom very closely resembles the

spectral shape and magnitude of the modeled spectrum for 1

mg Chl/m3 chl with 40% Trichodesmium and 60% other

phytoplankton (Figure 6.1d). As predicted by the model,

nLw412remains relatively low inside the bright feature but is

higher than nLw555 outside. However, nLw555 inside the

apparent Trichodesmium bloom is much lower than the model

prediction. The absolute magnitude criteria and the relative

magnitude criteria are clearly associated with the shape

criteria, indicating that the combination of these coherently

describe a Trichodesmium induced optical feature at spatial

scales of 10s of kilometers.

Application to South Atlantic Bight

During a cruise in the South Atlantic Bight in the fall of

1998, we encountered a Trichodesmium bloom along the 200

m isobath from about 33°N to 30°N. The bloom developed

after a series of bright sunny and calm days beginning the 25th

of October. As we traveled south from Beaufort NC, doing a

series of transects across the shelf; we occasionally saw and

sampled thin accumulations of Trichodesmium along the

continental shelf edge. On the 30th of October, we

encountered dense accumulations of Trichodesmium at the

surface in the morning during a period of exceptionally calm

weather, but these accumulations disappeared as the wind

picked up after 11:00. On the 31st of October, we observed

isolated accumulations a few meters wide, throughout the day

as we traveled south about 200 km along the shelf break. On

the 1st of November, there were dense blooms several 100

meters wide that turned the water greenish yellow in patches.

By the morning of the 3rd of November, the winds had picked

up and a low pressure front passed through the region on the

night of the 5th of November accompanied by wind speeds of

over 30 knots. After this no surface accumulations were

observed for the next 10 days. The OC2 derived chlorophyll

image (Figure 3 at

http://www.usc.edu/dept/LAS/biosci/tricho/manuscripts/)

shows the bottom reflection from the Little Bahamas Bank (2)

as an apparent high chlorophyll feature. The shallow turbid

coastal waters (4, 5) can also be seen as apparent high

chlorophyll along the coast. The Gulf Stream (6) can be

recognized by its low (0.1 mg Chl/m3

) chlorophyll

concentration, while the continental shelf generally appears to

have a higher chlorophyll concentration between 0.4 and 0.8

mg Chl/m3

. The turbid waters around Cape Canaveral (3)

have a much higher apparent chlorophyll concentration of

about 4 mg Chl/m3

.

The Trichodesmium bloom is seen separated from coastal

turbid waters in the chlorophyll image (Figure 4a at

http://www.usc.edu/dept/LAS/biosci/tricho/manuscripts/) by

about 20 km. It appears to lie just inshore of the Gulf Stream

edge, and along the continental shelf edge. Application of our

classification scheme reveals the bloom as a linear feature 100

km long and 20 km wide with a scalloped eastern edge (Figure

4b). The latter characteristic may be due to low amplitude

Gulf Stream meanders. There appears to be a small east-west

gradient in chlorophyll concentration, with the Trichodesmium

specific chlorophyll ranging from 1.5 to 2.5 mg Chl/m3

. A

comparison of Figures 4a and 4b (at

http://www.usc.edu/dept/LAS/biosci/tricho/manuscripts/)

shows that Trichodesmium makes up only a small part of the

total chlorophyll seen in this region. Bottom reflections in the

Little Bahamas Bank also appear to satisfy the classification

criteria as it too shows up in the classification image.

Normally, bottom reflection effects would be masked out by

the depth criteria in the classification scheme, but here it

appears that parts of the bathymetric data are inaccurate, as

only a part of the bank is masked.

Two transects, one east-west (Figure 5a and 5b at

http://www.usc.edu/dept/LAS/biosci/tricho/manuscripts/) and

the other north-south were taken through the 30 October

SeaWiFS image of the bloom using the Rline procedure of

SeaDAS. The east west transect very dramatically reveals the

difference between Trichodesmium dominated water and other

water types. The transect begins in the Gulf Stream (0-28 km)

where chlorophyll concentration is low (~0.1 mg Chl/m3

),

nLw443 and nLw412 are higher than the other bands,

consistent with low Trichodesmium concentrations. Then as

we move shoreward into the region between 30 and 45 km, the

nLws increase in all bands except 412 and the chlorophyll

concentration increases from 0.1 to about 0.5 mg Chl/m3

. The

spectral shape and magnitudes of nLws in this region

corresponds to that predicted for moderate concentrations of
SIMBIOS Project Annual Report

46

Trichodesmium. Farther shoreward, between 45 and 60 km,

all the nLw decrease in magnitude but the chlorophyll

concentration remains about the same, between 0.5 and 0.7 mg

Chl/m3

, consistent with the spectral shape of other

phytoplankton. Finally, close to shore, nLw490, nLw510, and

nLw555 as well as the calculated chlorophyll values increase

indicating turbid, high chlorophyll waters. The spectral shape

is almost the mirror image of the Gulf Stream waters with

nLw555 being the highest followed by nLw510, nLw490, and

nLw443. The second transect runs north-south (Figure at

http://www.usc.edu/dept/LAS/biosci/tricho/manuscripts/),

along the bright feature from near its northern edge to the

south into nonbloom waters. As in the Coral Sea case, it

appears that when the Trichodesmium concentration falls

below a threshold, the magnitude of nLw510 gets lower than

the magnitude of nLw443 and thus the classification criteria is

no longer satisfied even though there is probably

Trichodesmium present in the water (between 26 and 40 km in

the transect). Thus, the classification tends to create

artificially sharp boundaries.

Shipboard observations from the SAB

Two National Data Buoy Center weather buoys provided

wind speed, direction, wave heights and atmospheric pressure

in the region. Buoy 41008 is located at the Grays Reef

National Marine Sanctuary (31.4N, 80.87W), 40 nautical

miles southeast of Savannah. Buoy 41009 is located 20

nautical miles east of Cape Canaveral at 28.5N, 80.18 W. The

initial patch originated almost exactly at the location of buoy

41009. The maximum water temperature measured at buoy

41009 was 27.6°C and the minimum was 25.3°C between 25

October and 6 November. The average temperature during

this period was 26.6°C. On days when we saw visible

accumulations of Trichodesmium at the surface, bucket

samples were taken for enumeration using an epifluorescence

microscope.

The first visible accumulation of Trichodesmium was

observed on the morning of 30 October. At 9:45 AM, the

patch was a few meters wide but stretched for many

kilometers along the shelf break. A surface sample from the

densest part had 6700 trichomes/L (Table 6.1). The water was

glassy calm with less than 1-knot wind. Trichodesmium was

extremely patchy as the total chlorophyll measured at this

station from surface water outside the patch was only 0.37 mg

Chl/m3 – about 1/6th the calculated Trichodesmium specific

chlorophyll. By the time the satellite overpass at 12:30, the

wind had picked up to 7 knots. The Trichodesmium patches

disappeared and the surface sample only had 13 trichomes/L.

The classification did not show any evidence of

Trichodesmium in the SeaWiFS image at this location. On the

31st, although we observed visible accumulations of

Trichodesmium through the day while transiting along the

shelf break, the surface sample at 16:40 coinciding with the

SeaWiFS overpass contained only 740 trichomes/L. Still, the

Trichodesmium specific chlorophyll made up 43% of the total

chlorophyll measured at this station. On the 1st November, the

seas were very calm from early morning through early

afternoon and we observed large accumulations of

Trichodesmium during this period. At 9:15 AM, we were in

the middle of a very large accumulation and a surface bucket

sample contained 12300 trichomes/L. Here, Trichodesmium

specific chlorophyll was 71% of the total chlorophyll

measured at the station. However, the skies clouded over

before the SeaWiFS overpass and only a small section of one

of the patches is visible in the SeaWiFS image.

6.4 DISCUSSION

Optical Model

Subramaniam et al 1(999b) showed that there were

significant differences in the hyperspectral remote sensing

reflectance spectra of Trichodesmium versus other

phytoplankton at chlorophyll concentrations of 1 mg Chl/m3

and above, and that the differences were very pronounced at

high, bloom like concentrations of 10 mg Chl/m3. The model

that we present here was parameterized for five bands of 10

nm width matched to the SeaWiFS sensor. When the

hyperspectral data was reduced to five bands, some of the

obvious differences such as the pronounced reflectance peak

around 470 and 530 nm and the trough around 510 nm

disappeared due to the reduced spectral resolution. Other

differences between the model of Subramaniam et al. (1999b)

and the one presented here include a different

parameterization of sky reflectance - here we have followed

the method of Kirk (1994) rather than that of Carder and

Steward (1985). Also, in the present model we have used an

average of the CDOM “slope parameters” reported by Nelson

and Gaurdia (1995) representative of the mid shelf in the

South Atlantic Bight rather than the open ocean or extremely

turbid waters end members used in Subramaniam et al

(1999b). Finally, we restructured the model to take into

account mixed populations of Trichodesmium and other

phytoplankton. As discussed in section 4.2 below, considering

mixed populations is extremely important at various spatial

scales.

A comparison of the model predicted and satellite derived

normalized water leaving radiances leads us to believe that the

model may be over predicting nLw555. This could be because

the model exaggerates the contribution of phycoerythrin

fluorescence at chlorophyll concentrations of 1-2 mg chl/m3

.

The backscatter+fluorescence spectra measurements detailed

in Subramaniam et al (1999b) were made on a concentrated

collection of colonies in a small volume (about 20

colonies/mL). This is the equivalent of a 100 mg Chl/m3

. The

fluorescence observed at such high concentrations may not be

linearly scalable to lower chlorophyll concentrations between

0.5 and 2 mg Chl/m3

. However, it should be noted that the

hyperspectral model of Subramaniam et al (1999b)

corresponds extremely well both in shape and in magnitude
SIMBIOS Project Annual Report

47

with field measurements of a Trichodesmium bloom in the

Caribbean Sea (Navarro, 1998).

Image Classification

Subramaniam et al (1999b) found that it would not be

possible to distinguish between Trichodesmium and other

phytoplankton based on optical characteristics alone at

chlorophyll concentrations below about 0.8 mg Chl/m3

 (16

colonies/L). An algorithm developed from the optical model

of Subramaniam et al (1999b) and applied to global level 3 (8

day, 9 km binned global area coverage, "quality controlled")

data revealed very few "hits". This could be because the

optical model predictions are wrong, because of incorrect

atmospheric correction, or because the eight-day temporal

binning and the nine km spatial binning smeared the

Trichodesmium signal out. In addition, dense surface blooms

of Trichodesmium would probably fail the standard quality

control procedures and be eliminated from the level 3 data or

could be misclassified as clouds.

Because the theory based optical model was not helpful in

developing an algorithm, we tried to use an empirical image

based approach. However, at present, we do not have ground

truth data with chlorophyll measurements and cell counts for

even one mid ocean Trichodesmium bloom with a coinciding

SeaWiFS image. Hence, even though coastal waters are

extremely complex, we had to use coastal scenes with putative

Trichodesmium blooms to develop our classification scheme.

The use of actual SeaWiFS imagery overcomes two problems

with using a purely theoretical approach. The first problem is

to apply the appropriate atmospheric correction scheme to

correctly derive the normalized water leaving radiance from

the satellite sensor measurement of total radiance. In turbid

waters, the water leaving radiance in the red and near infrared

is not negligible, and the incorrect atmospheric correction

results in an underestimate of the water leaving radiance,

especially in the blue bands. Waters containing significant

accumulations of Trichodesmium are golden brown in color

and would have a significant water leaving radiance in the red

and near infrared resulting in an underestimate of the water

leaving radiance in the blue and green bands. Considering that

the most prominent signal that distinguishes Trichodesmium

from other phytoplankton is its high backscatter, it is not

surprising that we were not able to map Trichodesmium

blooms using an algorithm derived from the optical model.

We have observed that a surface cyanobacterial bloom in the

Baltic Sea has enough signal at 865 nm that portions of the

bloom was misidentified and masked as clouds.

The second problem that the empirical approach

overcomes is that of mixed populations. The question of

mixed populations can be discussed at various spatial scales

ranging from a point measurement all the way to a 9-km2

level 3 pixel. As seen at the station occupied on 1 November

in the South Atlantic Bight (Table 6.1), even during a

significant Trichodesmium accumulation with 12700

trichomes/L, Trichodesmium only contributed 71% of the total

chlorophyll in that bucket sample. Thus at a very local scale,

mixed populations can be discussed in terms of number of

cells from each species of phytoplankton present in a small

volume of water. However, mixed populations in a remote

sensing context can also be discussed in terms of spatial

inhomogenity. We have observed that while on the one hand,

small concentrations of Trichodesmium (say 1 colony/L or

less) are homogeneously distributed in the water column, as

the concentrations increase, Trichodesmium tends to

accumulate in patches near the surface. Thus on 30 October,

while there was 1.83 mg Trichodesmium specific chlorophyll

per m3

 in the sample taken for cell counts, there was only 0.37

mg Chl/m3

 in a water sample taken a few minutes later,

presumably outside the Trichodesmium patch. Thus, unlike

most other phytoplankton that are homogeneously distributed

in the near surface water column, buoyant cyanobacterial such

as Trichodesmium have a very patchy horizontal distribution

within a 1 km2

 SeaWiFS pixel and the issue of mixed

populations should also be discussed in terms of subpixel

variability - the percent area within a pixel that is covered by

Trichodesmium. It is unclear to us how the SeaWiFS sensor

responds to subpixel variability. Subpixel variability occurs

when the satellite sensor’s instantaneous field of view is

partially filled with surface accumulations of Trichodesmium

with intervening clear water. The reflectivity observed by the

sensor is a combination of both the Trichodesmium and ocean

water reflectances which are integrated by the sensor. The

exact sensor response depends on the relative amounts of

Trichodesmium and clear water, the distribution of both in the

sensor’s field of view, and the integrating aperture function of

the sensor. Consequently, we get reflectance values for a

pixel containing some Trichodesmium that has neither the

characteristics of Trichodesmium nor other phytoplankton nor

clear water. At the very best, the relationship between the

multispectral data and the ocean water, phytoplankton

biomass, and Trichodesmium biomass is complex and these

three signals may not be separable by linear methods or simple

band ratio type algorithms. In the empirical classification

approach outlined above, we cannot mechanistically account

for atmospheric correction or subpixel variability, but these

are embedded into the data used.

6.5 CONCLUSIONS

It is generally thought that organism specific remote

sensing is not possible except in the case of organisms with

exceptional optical properties that set them apart.

Trichodesmium is an excellent example of such an organism

with a unique combination of absorption, scattering and

fluorescence properties. In this paper, we have demonstrated

that it is possible to identify Trichodesmium when it is present

in sufficient quantities (i.e. > 15 colonies/L), even in optically

complex coastal waters rich in CDOM and sediments.

The classification scheme discussed here may not be

universally applicable, being relevant only to coastal shelf
SIMBIOS Project Annual Report

48

waters. This is not surprising considering that the training

pixels are from the shelf. The present classification scheme is

tuned to mixed populations in which other phytoplankton and

CDOM make a more significant contribution to the optical

signal than would be found in a Trichodesmium bloom in the

middle of the ocean. We are now working on an open ocean

classification criteria as well as a more robust universal

algorithm using the optimization/inverse modeling technique

of Maritorena et al. (2000).

REFERENCES

Capone, D. G., J. P. Zehr, H.W. Paerl, B. Bergman, and E.J.

Carpenter, 1997: Trichodesmium, a globally significant

marine cyanobacterium. Science 276, 1221-1229.

Carder, K. L. and R. G. Steward, 1985. A remote-sensing

reflectance model of a red-tide dinoflagellate off West

Florida. Limnology and Oceanography 30(2), 286-298.

Carpenter, E. J. (1983). Physiology and ecology of marine

planktonic Oscillatoria (Trichodesmium). Marine Biology

Letters 4(2), 69-85.

Dunstan, W. M. and J. Hosford, 1977: The distribution of

planktonic blue green algae related to the hydrography of

the Georgia Bight. Bulletin of Marine Science 27(4), 824-

829.

Fu, G., K.S. Baith, and C.R. McClain, 1998: SeaDAS: The

SeaWiFS Data Analysis System, Proceedings of The 4th

Pacific Ocean Remote Sensing Conference, Qingdao,

China, July 28-31, 73-79.

Furnas, M. J., 1992: Pelagic Trichodesmium (=Oscillatoria) in

the Great Barrier Reef Region. In: E.J. Carpenter, D.G.

Capone, and J.G. Rueter (Eds), Marine Pelagic

Cyanobacteria: Trichodesmium and other Diazotrophs.

Kluwer Academic Publishers, Dordrecht. pp 265-272.

Glibert, P. M., J.M. O'Neil, C.A. Heil, 2000: Stimulation of

Dinoflagellate Blooms During and After Blooms of the

Diazotrophic Cyanobacteria Trichodesmium. EOS 80(49),

OS170.

Hood, R.R., A.F. Michaels, D.G. Capone, 2000: Answers

sought to the enigma of marine nitrogen fixation. EOS

81(133), 138-139.

Hulbert, E. M., 1967: Some notes on the phytoplankton of the

southeastern coast of the United States. Bulletin of

Marine Science 17, 330-337.

Kirk, J. T. O., 1994: Light and Photosynthesis in Aquatic

Ecosystems, Cambridge University Press.

Kuchler, D. A. and D. L. B. Jupp, 1988: Shuttle photograph

captures massive phytoplankton bloom in the Great

Barrier Reef. International Journal of Remote Sensing.

9(8): 1299-1301.

Marshall, S. M., 1933: The production of microplankton in the

Great Barrier Reef region. In: Scientific Reports of the

Great Barrier Reef Expedition, Vol 2, pp 111-157.

Marshall, H. G., 1971: Composition of phytoplankton off the

Southeastern coast of the United States. Bulletin of

Marine Science 21, 806-825.

Maritorena, S., Siegel, D.A., Peterson, A.R. & LorenziKayser, M. 2000: Tuning of a pseudo-analytical ocean

color algorithm for studies at global scales. Presented at

the 2000 AGU Ocean Sciences Meeting (OS12M-05),

San Antonio TX.

Navarro Rodriguez, A. J.,1998: Optical Properties of

Photosynthetic Pigments and Abundance of the

Cyanobacterium Trichodesmium in the Eastern Caribbean

Basin. Ph.D. Thesis, University of Puerto Rico,

Mayaguez, Puerto Rico.

Nelson, J. R. and S. Guarda, 1995: Particulate and dissolved

spectral absorption on the continental shelf of the

southeastern United States. Journal of Geophysical

Research 100 (C5), 8715-8732.

O'Reilly, J. E., S. Maritorena, B.G. Mitchell, D.A. Siegel, K.L.

Carder, S.A. Garver, M. Kahru, C. R. McClain, 1998:

Ocean color chlorophyll algorithms for SeaWiFS. Journal

of Geophysics 103(C11), 24937-24953.

Revelante, N. and M. Gilmartin, 1982: Dynamics of

phytoplankton in the Great Barrier Reef lagoon. Journal

of Plankton Research 4, 47-76.

Subramaniam, A., E. J. Carpenter, D. Karentz, and P.G.

Falkowski, 1999a: Optical Properties of the Marine

Diazotrophic Cyanobacteria Trichodesmium spp.; I -

Absorption and Spectral Photosynthetic Characteristics.

Limnology and Oceanography 44(3): 608-617.

Subramaniam, A., E. J. Carpenter, and P.G. Falkowski, 1999b:

Optical Properties of the Marine Diazotrophic

Cyanobacteria Trichodesmium spp.; II - A Reflectance

Model for Remote-Sensing. Limnology and

Oceanography 44(3), 618-627.
SIMBIOS Project Annual Report

49

Modelled and SeaWiFS Derived Normalized Water Leaving Radiance Spec

Other PhytoplanktonnLw (μW/cm 2/nm/sr)

0

1

2

3

O.5 mg Chl/m3

1.0 mg Chl/m3

1.5 mg Chl/m3

2.0 mg Chl/m3

2.5 mg Chl/m3

3.0 mg Chl/m3

4.0 mg Chl/m3

5.0 mg Chl/m3

7.0 mg Chl/m3

10.0 mg Chl/m3

TrichodesmiumnLw (μW/cm 2/nm/sr)

0

1

2

3

4

5

O.5 mg Chl/m3

1.0 mg Chl/m3

1.5 mg Chl/m3

2.0 mg Chl/m3

2.5 mg Chl/m3

3.0 mg Chl/m3

4.0 mg Chl/m3

5.0 mg Chl/m3

7.0 mg Chl/m3

10.0 mg Chl/m3

Wavelength (nm)

400 420 440 460 480 500 520 540 560 580

Model Predictions ofMixed Populations

0

1

2

3

10% Other Phytoplankton

30% Other Phytoplankton

50% Other Phytoplankton

60% Other Phytoplankton

70% Other Phytoplankton

90% Other Phytoplankton

100% Other Phytoplankton

Trichodesmium 1.8 mg Chl/m 3

Model Prediction and SeaWiFS Derived Highly Reflective Phenomena

0

1

2

3

(a)

(b)

(c)

(d)

Trichodesmium

1.8 mg Chl/m3

Sediments 

Celtic Sea 

Coccolithophore Bloom

Nodularia 8.3 mg Chl/m 3 

Model-Phytoplankton 0.5

Model-Phytoplankton 1.0

Model-Phytoplankton 1.5

Model-Trichodesmium 0.5

Model-Trichodesmium 1.0

Model-Trichodesmium 1.5

Figure 6. 1 A) Modeled remote sensing reflectance for “other phytoplankton”; B) Modeled remote sensing reflectance for

Trichodesmium; C) Modeled and SeaWiFS measured Tricho and other phytoplankton, bright features; D) Modeled remote

sensing reflectance for varying proportions of other phytoplankton to Trichodesmium when total chlorophyll is 1 mg Chl/m3
SIMBIOS Project Annual Report

50

Table 6.1 In situ measurements in the South Atlantic Bight

Station Location Trichodesmium

Count

(Trichomes/L)

Measured Total

Clorophyll

Mg Cl/m3

SeaWiFS Derived

Chlorophyll

(mg Chl/m3)

Date Time

GMT

Latitude Longitude

Wind

speed

(m/s)

Sea

water

Temp.

10/30 14:45 31.997 79.095 0.5 26.6 6700 0.366 -

10/30 17:30 31.772 79.313 3.5 26.6 13 0.426 0.303

10/30 19:40 31.700 79.460 3.5 26.7 155 0.906 -

10/31 16:40 30.009 80.500 5.5 25.4 740 0.435

11/1 14:15 30.017 80.500 0.5 25.3 12300 4.318 -

This research was supported by the

SIMBIOS NASA contract # 97131

PEER REVIEWED PUBLICATIONS

Carpenter, E. J., J.P. Montoya, J. Burns, M. Mulholland,

A.Subramaniam, D.G. Capone.1999. Extensive bloom of

a N2 fixing symbiotic association in the tropical Atlantic

Ocean. Mar. Ecol. Prog. Series. Vol.185, 273-283.

Capone, D.G., A. Subramaniam, J.P. Montoya, M. Voss, C.

Humborg, F. Pollehne and E.J. Carpenter, 1998: An

extensive bloom of the diazotrophic cyanobacterium,

Trichodesmium erythraem, in the central Arabian Sea

during the spring intermonsoon. Marine Ecology

Progress Series. 172, 281-292.

Zehr, J., E.J. Carpenter & T. Villareal. 2000. New perspectives

on nitrogen-fixing microorganisms in tropical and

subtropical oceans. Trends in Microbiol, 8, 68-73.

Accepted

Hood, R. R., A. Subramaniam, L. R. May, E. J. Carpenter and

D. G. Capone, 2000: Remote Estimation of Nitrogen

Fixation by Trichodesmium. Deep-Sea Research (1st

Special Issue on the U.S. JGOFS SMP).

Subramaniam, A., R. R. Hood, C. W. Brown, E. J. Carpenter

and D. G. Capone, 2000: Detecting Trichodesmium

Blooms in SeaWiFS Imagery. Deep-Sea Research. (1st

Special Issue on the U.S. JGOFS SMP).

PRESENTATIONS

Cota, G.F., W.G. Harrison, T. Platt, and S. Sathyendranth,

1998: OCTS ocean color and AVHRR observations in the

Labrador Sea. Ocean Sciences Meeting, San Diego, EOS

Transactions, 79(1), Suppl: OS81.

Dupouy, C., J. Neveux, B. Fougnie, S. Colzy, and A.

Subramaniam. 1999: Trichodesmium: an oceanic nitrogen

fixer as detected by POLDER. Symposium ALPS'99,

Colloque International et Ateliers, CNES, 18-23, January

1999, Meribel, France, Ocean Color P-01, pp. 1-5.

Dupouy, C., J. Neveux, A. Subramaniam, M. R. Mulholland,

J. P. Montoya, L. Campbell, E. J. Carpenter and D. G.

Capone, 2000: Satellite Captures Trichodesmium Blooms

in the Southwestern Tropical Pacific. EOS 81(2), 13-16.

Flatau, P., B. G. Mitchell, A. Subramaniam, J. Weldon, J.

Wieland, K. Voss, R. Frouin, and J. Porter, 1999:

Combined ocean color and atmospheric radiometeric

measurements on R/V Ronald Brown ship during the

Indian Ocean Experiment (INDOEX). 10th Conference

on Atmospheric Radiation , 28th June - 2nd July 1999.

Subramaniam, A. and D.G. Capone, 2000: Global Occurrence

of Trichodesmium During The 1997-1998 El Nino-La

Nina Cylce. SOLAS Open Science Meeting, Damp

Germany.

Subramaniam, A., J. Nolan, J. Wieland, M. Kahru, B.G.

Mitchell, 2000: An Optical Bio-Geographical Description

of the Indian Ocean During the 1999. Spring

Intermonsoon Ocean Sciences 2000.

Subramaniam, A., S. Kratzer, E. J. Carpenter and E.

Söderbäck, 2000: Remote sensing and optical in-water

measurements of a cyanobacteria bloom in the Baltic Sea.

Proceedings of the Sixth International Conference on

Remote Sensing for Marine and Coastal Environments,

Charleston, SC, Veridian ERIM International.
SIMBIOS Project Annual Report

51

Chapter 7

Bio-Optical Measurements at Ocean Boundaries in Support of

SIMBIOS

Francisco P. Chavez, Peter G. Strutton, and Brian M. Schlining

Monterey Bay Aquarium Research Institute, Moss Landing, California

7.1 INTRODUCTION

The equatorial Pacific is a major component of global

biogeochemical cycles, due to upwelling that occurs from

the coast of South America to beyond 180°. This upwelling

has significant implications for global CO2 fluxes (Tans et

al., 1990; Takahashi et al., 1997; Feely et al., 1999), as

well as primary and secondary production (Chavez and

Barber, 1987; Chavez and Toggweiler, 1995; Chavez et al.,

1996; Dugdale and Wilkerson, 1998; Chavez et al., 1999;

Strutton and Chavez, in press). In addition, this region of

the world’s oceans represents a large oceanic province over

which validation data for SeaWiFS are necessary. This

project consists of a mooring program and supporting

cruise-based measurements aimed at quantifying the

spectrum of biological and chemical variability in the

equatorial Pacific and obtaining validation data for

SeaWiFS. The project has the following general objectives:

• to understand the relationships between physical

forcing, primary production, nutrient supply and the

exchange of carbon dioxide between ocean and

atmosphere in the equatorial Pacific

• to describe the biological and chemical responses to

climate and ocean variability

• to describe the spatial, seasonal and inter-annual

variability in near surface plant pigments, primary

production, carbon dioxide and nutrient distributions,

and

• to obtain near real-time bio-optical measurements for

validation of SeaWiFS and subsequent ocean color

sensors.

7.2 RESEARCH ACTIVITIES

Moorings

Chavez et al. (1998; 1999; 2000) and McClain and

Fargion (1999a, p.44) describe the configuration of the

MBARI bio-optical and chemical instruments deployed at

0°, 155°W and 2°S, 170°W; two of the >70 moorings

which form the Tropical Atmosphere Ocean (TAO) array.

In addition, MBARI has also maintained smaller biooptical packages at sites in the equatorial Pacific: 2°N,

180°; 2°S, 140°W, 2°N, 140°W and 2°N, 110°W (Strutton

et al., submitted).

From 0°, 155°W and 2°S, 170°W, daily local noon

(ie approximate time of SeaWiFS overpass) bio-optical and

chemical data are transmitted via service ARGOS in near

real time to MBARI, and then via automated FTP to the

SeaBASS database, after processing and quality control.

Higher frequency, publication-quality data (currently

recorded at 15-minute intervals) are recovered at

approximately six month intervals, and sent to the

SeaBASS database after processing and quality control.

Derived products, such as Kλ, OC2V4 chlorophyll, mean

chlorophyll over the upper 20m of the water column

(Morel, 1988), water leaving radiance (Lw), and remote

sensing reflectance (Rrs) are included in these data files for

validation efforts. In the past, Lw has been calculated via

three different methods (McLain and Fargion, 1999b), but

is currently calculated only as follows. The diffuse

attenuation coefficient (Kλ) over the upper 20m of the

water column is calculated using Ed3m+ and Ed20m, then

using this Kλ, Lu20m is extrapolated back to just below the

surface. This value (Lu0m-) is then multiplied by 0.544 to

account for transmission across the air-water interface, and

hence derive Luom+. Of the three methods previously used,

this has been shown to be the most reliable, because the

3m+ and 20m instruments are less susceptible to fouling

than the OCR-100s moored at ~1.5m.

Optical Profiling Measurements

On mooring maintenance cruises, optical profiles of

the upper 100 to 200 m of the water column are performed,

daily when possible, close to local noon. The instrument

used is the Satlantic SeaWiFS Profiling Multispectral

Radiometer (SPMR). Profile data are processed using

Satlantic’s ProSoft software, and a suite of derived

products, including diffuse attenuation coefficients, water

leaving radiances (Lwn) and light penetration depths.

Parameters of interest (mostly Lwn) are provided to NASA

post-cruise, and the profile data are archived at MBARI
SIMBIOS Project Annual Report

52

May-98 Jun-98 Jul-98 Aug-98 Sep-98 Oct-98 

SST [°C]

22

24

26

28

30

32

Chl [

μg l-1]

0

1

2

3

4

v [mm s-1]

-500

-250

0

250

500

SST

Chl

v

A A

A

A

B

B

Figure 7.1 Time series of SST [°C] (•) and mooring-derived chlorophyll [μg l-1] (•) at 2°N 110°W

plotted with meridional velocity from 50 m ADCP data at 0° 110°W [mm s-1] (), May to September 1998.

Extremely high chlorophyll concentrations are often observed at the leading or trailing edges of the cool SST

anomaly (labeled A), or in the coolest sea surface waters associated with the core of the main equatorial upwelling

bloom (labeled B). Positive values of v denote flow to the north at the equator, which should be indicative of

northwards advection of cool, high-chlorophyll waters from the equatorial upwelling tongue.

1998 1999 2000

0

0.5

1

1.5

Year

Chl a (

μg l −1)

Mooring CHL 

SeaWiFS CHL 

in situ CHL 

Mooring Running Mean

SST 

22

23

24

25

26

27

28

29

30

31

SST (oC)

Figure 7.2 The time series of chlorophyll derived from the MBARI optical instruments on the TAO mooring at 0°

155°W (circles, solid line represents a moving average). For comparison, SeaWiFS chlorophyll for the mooring

location is also plotted (triangles). The time series of SST indicates that the highest chlorophyll concentrations

occurred during the retreat of the 1997-98 El Niño, in mid 1998. SeaWiFS and mooring chlorophyll exhibit

relatively good agreement over more than an order of magnitude of chlorophyll concentrations.
SIMBIOS Project Annual Report

53

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Mooring Chl (μg l−1)

SeaWiFS Chl (μg l−1

)

0o 155oW 

2o

S 170oW

Figure 7.3 Comparison of SeaWiFS chlorophyll with mooring-derived chlorophyll for the

MBARI instruments at 0° 155°W (circles) and 2°S 170°W (triangles). The data have been subject

to the quality control described.

0 0.1 0.2 0.3 0.4 0.5 0.6 0

0.1

0.2

0.3

0.4

0.5

0.6

Y = 0.823X + 0.009

r

2

 = 0.594

Modeled Chl (μg l−1

)

Measured Chl (μg l−1

)

Figure 7.4 Comparison of surface chlorophyll concentrations obtained from SPMR optical cast

data (x axis) with in-situ extracted chlorophyll (y axis).
SIMBIOS Project Annual Report

54

along with existing optical profiles from almost every

oceanic province.

In situ Measurements

Table 7.1 summarizes the cruises undertaken by

MBARI thus far in support of SIMBIOS. Essentially, the

cruise-based measurements consist of chlorophyll (using

the fluorometric method described by Chavez et al. (1995))

and nutrient profiles (8 depths, 0-200m) obtained at CTD

stations between 8°N and 8°S across the Pacific from

95°W to 165°E. On selected cruises (the 155°W and

170°W meridional transects), primary productivity (14C)

measurements are also performed. These data are archived

at MBARI and the chlorophyll data provided to the

SeaBASS database for algorithm development.

7.3 RESEARCH RESULTS

Biogeochemical Cycles

Several publications describing ecosystem variability

in the equatorial Pacific have been produced under

MBARI’s SIMBIOS funding. Chavez et al. (1998) used

mooring data from 0°, 155°W to describe the biologicalphysical coupling observed in the central equatorial Pacific

during the onset of the 1997-98 El Niño. Chavez et al.

(1999) combined the physical, biological and chemical

data from moorings, ships and SeaWiFS to provide a

comprehensive view of the ecosystem’s response to the

extreme physical forcings experienced during the 1997-98

El Niño. Strutton and Chavez (in press) summarized the in

situ cruise measurements spanning the period from

November 1996 to December 1998, and used these data to

describe the perturbations to chlorophyll, nutrients and

productivity during the same time period. Strutton et al.

(submitted) used time series from the smaller Satlantic biooptical packages (see above) and SeaWiFS imagery, to

quantify the extreme anomalies in chlorophyll associated

with the passage of tropical instability waves (TIWs) and

the second half of 1998 and 1999. These data not only

quantified the magnitude of the chlorophyll anomalies

observed, but also helped to elucidate the mechanisms

potentially responsible for the concentration of chlorophyll

in association with TIWs. Figure 7.1 shows an example of

the time series from 2°N 110°W where the relationship

between TIWs and enhanced chlorophyll was particularly

obvious between June and October 1998.

In addition to the manuscripts just described, Chavez

et al. (2000) documented the design, and demonstrated

efficacy of the shutter mechanism which protects the 20m

radiometer on the moorings from fouling.

SeaWiFS Calibration/Validation

Figure 7.2 shows the time series collected thus far

from the mooring at 0° 155°W. The two methods agree

particularly well over a range of chlorophyll concentrations

from ~0.05 to >1 μg l-1. This agreement is further

quantified in figure 7.3, which directly shows the

comparison of the two methods for both the mooring at 0°

155°W and 2°S 170°W.

McClain and Fargion (1999b) showed matchup data

derived from optical profiles of the SPMR in the equatorial

Pacific. These data indicated excellent agreement between

the satellite- and profile-derived water-leaving radiance

values. Similarly, figure 7.4 compares the surface

chlorophyll values obtained from SPMR casts with the

extracted chlorophyll samples obtained at the same

location.

7.4 CONCLUSIONS

Data collection will continue as for the period 1998-

2000. The two major mooring installations at 0°, 155°W

and 2°S, 170°W are now operating well and several

improvements are planned for the next three year period of

funding. These include the possible addition of

hyperspectral instruments to better support validation

efforts for future ocean color missions and increase the

suite of biologically-relevant parameters that can be

derived from the mooring data. The program of cruisebased measurements of chlorophyll, nutrients, primary

productivity and bio-optical profiles will continue on up to

eight equatorial Pacific cruises during 2000-2003, with

scheduled SeaWiFS LAC where applicable, to enhance the

probability of obtaining valid matchups.

During 1999 and 2000 our data processing and data

provision capabilities have improved, & the majority of the

mooring data can now be downloaded and plotted via the

at: http:/bog.shore.mbari.org/servlet/NetcdfIndexServlet

During 2000 our quality control routines have been

improved. The following quality control procedures are

currently applied:

• Measured surface-incident irradiance (Es) must be

less than 1.15 times modeled Es (Frouin, 1989).

• Kλ must be greater than that of pure water (Morel,

1988).

• Derive OC2 Chlorophyll for the Rrs ratios of

412/555, 443/555, 490/555, and 510/555. The

coefficient of variance is calculated for each sample.

Coefficients of variances less than 0.4 are acceptable.

• In coming years we hope to continue improvement of

these criteria and use the results to perhaps modify

mooring and instrument configurations.
SIMBIOS Project Annual Report

55

REFERENCES

Chavez, F.P., and R.T. Barber, 1987: An estimate of new

production in the equatorial Pacific, Deep-Sea

Research, 34, 1229-1243.

Chavez, F.P., K.R. Buck, R.R. Bidigare, D.M. Karl, D.

Hebel, M. Latasa, L. Campbell and J. Newton, 1995:

On the chlorophyll a retention properties of glassfiber GF/F filters. Limnology and Oceanography, 40,

428-433.

Chavez, F.P. and J. R. Toggweiler, 1995: in Upwelling in

the Ocean: Modern Processes and Ancient Records

C. P. Summerhayes, K. C. Emeis, M. V. Angel, R. L.

Smith, B. Zeitzschel, Wiley & Sons, Chichester Eds.,

pp. 313-320.

Chavez, F.P., K.R. Buck, S.K. Service, J. Newton and

R.T. Barber, 1996: Phytoplankton variability in the

central and eastern tropical Pacific. Deep-Sea

Research, 43(4-6), 835-870.

Chavez, F.P., P.G. Strutton, and M.J. McPhaden, 1998:

Biological-physical coupling in the central equatorial

Pacific during the onset of the 1997-98 El Niño.

Geophysical Research Letters, 25(19), 3543-3546.

Chavez, F.P., P.G Strutton, G.E. Friederich, R.A. Feely,

G. Feldman, D. Foley and M.J. McPhaden, 1999:

Biological and chemical response of the equatorial

Pacific Ocean to the 1997-98 El Niño. Science, 286,

2126-2131.

Chavez, F.P., D. Wright, R. Herlien, M. Kelley, F. Shane,

and P.G. Strutton, 2000: A device for protecting

moored spectroradiometers from bio-fouling. Journal

of Oceanic and Atmospheric Technology. 17, 215-

219.

Dugdale, R.C. and F.P. Wilkerson, 1998: Silicate

regulation of new production in the equatorial Pacific

upwelling, Nature, 391, 270-273.

Feely, R.A., R. Wanninkhof, T. Takahashi and P. Tans,

1999: Influence of El Niño on the equatorial Pacific

contribution to atmospheric CO2 accumulation.

Nature, 398, 597-601.

Frouin, R., D.W. Ligner, and C. Gautier, 1989: A simple

analytical formula to compute clear sky total and

photosynthetically available solar irradiance at the

ocean surface. Journal of Geophysical Research, 94,

9731-9742.

McClain C.R. and G.S. Fargion, 1999a: SIMBIOS Project

1998 Annual Report. NASA Tech. Memo. 1999-

208645, Eds., NASA Goddard Space Flight Center,

Goddard, Maryland, 105pp.

McClain C.R. and G.S. Fargion, 1999b: SIMBIOS Project

1999 Annual Report. NASA Tech. Memo. 1999-

209486, Eds., NASA Goddard Space Flight Center,

Goddard, Maryland, 137pp.

Morel, A. 1988: Optical modeling of the upper ocean in

relation to its biogenous matter content (Case I

waters). Journal of Geophysical Research, 93,

10749-10768.

Strutton, P.G. and F.P. Chavez, 2000: Primary

productivity in the equatorial Pacific during the

1997-98 El Niño. J.Geophy. Research ( in press).

Tans, P.P., I. Y. Fung, T. Takahashi , 1990: Observational

constraints on the global atmospheric CO2 budget,

Science 247, 1431-1438.

Takahashi, T, R.A. Feely, R.F. Weiss, R. Wanninkhof,

D.W. Chipman, S.C. Sutherland and Takahashi,

1997: Carbon dioxide and climate change.

Proceedings of the National Academy of Sciences,

94, 8314-8319.
SIMBIOS Project Annual Report

56

Table 7.1. Summary of cruises during which in situ data have been obtained by MBARI in support of

SIMBIOS. All cruises were undertaken aboard the NOAA ship Ka’imimoana, with the exception of cruise

with ID ending in RB, which were aboard the NOAA ship Ronald H. Brown. Meridional transects indicate

the lines occupied by the ship. Along each line, CTD stations were performed approximately every degree

of latitude from 8°N to 8°S. Measurements consisted of extracted chlorophyll (Chl) plus nitrate, phosphate

and silicate (Nutrients) at 8 depths between 0 and 200m. On selected cruises, primary productivity (PP)

measurements were also made using 14C incubation techniques, and daily optical profiles with the Satlantic

Profiling Multispectral Radiometer (SPMR) were obtained. During GP7-98-KA, GP3-99-KA and GP7-99-

KA, a SIMBAD radiometer was also used, on loan from the SIMBIOS instrument pool.

Cruise ID Dates Meridional transects Measurements

GP6-97-KA 27-Sep-97 to 30-Oct-97 125°W and 140°W Chl, PP, Nutrients, SPMR

GP7-97-KA 06-Nov-97 to 17-Dec-97 155°W, 170°W and 180° Chl, PP, Nutrients, SPMR

GP1-98-KA 05-Feb-98 to 13-Mar-98 95°W and 110°W Chl, Nutrients

GP2-98-KA 18-Apr-98 to 20-May-98 125°W and 140°W Chl, Nutrients

GP3-98-KA 02-Jun-98 to 03-Jul-98 155°W and 170°W Chl, PP, Nutrients, SPMR

GP4-98-KA 07-Jul-98 to 03-Aug-98 165°E and 180° Chl, Nutrients

GP5-98-KA 05-Sep-98 to 09-Oct-98 125°W and 140°W Chl, Nutrients

GP6-98-RB 11-Oct-98 to 13-Nov-98 95°W and 110°W Chl, Nutrients

GP7-98-KA 19-Oct-98 to 13-Nov-98 155°W and 170°W Chl, PP, Nutrients, SPMR

GP8-98-KA 18-Nov-98 to 12-Dec-98 180° and 170°W Chl, Nutrients

GP1-99-KA 22-Jan-99 to 24-Feb-99 125°W and 140°W Chl, Nutrients

GP2-99-KA 30-Apr-99 to 04-Jun-99 95°W and 110°W Chl, Nutrients

GP3-99-KA 30-Jun-99 to 30-Jul-99 155°W and 170°W Chl, PP, Nutrients, SPMR

GP4-99-KA 04-Aug-99 to 01-Sep-99 165°E and 180° Chl, Nutrients

GP5-99-KA 09-Sep-99 to 14-Oct-99 125°W and 140°W Chl, Nutrients

GP7-99-KA 20-Oct-99 to 14-Nov-99 155°W and 170°W Chl, PP, Nutrients, SPMR

GP8-99-RB 1-Nov-99 to 10-Dec-99 95°W and 110°W Chl, Nutrients

GP9-99-KA 18-Nov-99 to 12-Dec-99 180° and 170°W Chl, Nutrients

GP1-00-KA 01-Feb-00 to 03-Mar-00 140°W and 125°W Chl, Nutrients

GP2-00-KA 12-Apr-00 to 19-May-00 110° and 95°W Chl, Nutrients

GP3-00-KA 15-Jun-00 to 12-Jul-00 155°W and 170°W Chl, PP, Nutrients, SPMR

GP4-00-KA 17-Jul-00 to 14-Aug-00 165°E and 180° Chl, Nutrients

GP5-00-KA 29-Aug-00 to 1-Oct-00 125°W and 140°W Chl, Nutrients

GP6-00-KA 14-Oct-00 to 14-Nov-00 155°W and 170°W Chl, PP, Nutrients, SPMR

GP7-00-RB 16-Oct-00 to 22-Nov-00 110°W and 95°W Chl, Nutrients

GP8-00-KA 17-Nov-00 to 13-Dec-00 165°E and 180° Chl, Nutrients
SIMBIOS Project Annual Report

57

This research was supported by the

SIMBIOS NASA contract # 97134

PEER REVIEWED PUBLICATIONS

Chavez, F.P., Wright, D., Herlien, R., Kelley, M., Shane, F.

and Strutton, P.G., 2000: A device for protecting moored

spectroradiometers from bio-fouling. Journal of Oceanic

and Atmospheric Technology. 17, 215-219.

Chavez, F.P., Strutton, P.G., Friederich, G.E., Feely, R.A.,

Feldman, G.C., Foley, D.G. and McPhaden, M.J., 1999:

Biological and chemical response of the equatorial Pacific

Ocean to the 1997-98 El Niño. Science, 286, 2126-2131.

Chavez, F.P., Strutton, P. G. and McPhaden, M. J., 1998:

Biological-physical coupling in the equatorial Pacific

during the onset of the 1997-98 El Niño. Geophysical

Research Letters. 25(19), 3543-3546.

Chavez, Francisco P., Peter G. Strutton and Michael J.

McPhaden, 1998: Biological-physical coupling in the

central equatorial Pacific during the onset of the 1997-98

El Niño. Geophysical Research Letters, 25(19),3543-

3546.

Accepted

Strutton, P.G. and Chavez, F.P. Primary productivity in the

equatorial Pacific during the 1997-98 El Niño. Journal of

Geophyiscal Research.

Submitted

Strutton, P.G., Ryan, J.P. and Chavez, F.P. Enhanced

chlorophyll at tropical instability wave fronts in the

equatorial Pacific. Geophysical Research Letters.

PRESENTATIONS

Chavez, Francisco, Gene Feldman, Michael McPhaden, David

Foley and Peter Strutton, 1998: Remote Sensing of the

Equatorial Pacific during 1997-98. AGU Fall Meeting,

San Francisco, CA.

Friederich, Gernot, Francisco Chavez, Peter Strutton and Peter

Walz, 1998: Bio-optical and Carbon Dioxide Time Series

From Moorings in the Equatorial Pacific During 1997-

1998. AGU Fall Meeting, San Francisco, CA.

Friederich, G E Walz, P M Strutton, P G Burczynski, M G

Chavez, F P Seasurface pCO2 Time Series from Moorings

in Equatorial and Coastal Upwelling Systems. AGU

Ocean Sciences meeting, San Antonio, Texas.

Strutton, P.G., Chavez, F.P. and McPhaden, M.J., 1998: In situ

measurements of primary productivity in the equatorial

Pacific during the 1997-98 El Niño. AGU Fall Meeting,

San Francisco, CA.

Strutton, P.G., Ryan, J.P., Polito, P. and Chavez, F.P., 2000:

High Chlorophyll in the Equatorial Pacific During 1998

Associated With Tropical Instability Wave Fronts. AGU

Ocean Sciences meeting, San Antonio, TX.

Strutton, Peter G., Francisco P. Chavez and Michael J.

McPhaden, 1998: Biological-physical coupling in the

central equatorial Pacific during the 1997-98 El Niño.

Ocean Optics XIV, Kailua-Kona, HI.

Strutton, Peter G., Francisco P. Chavez and Michael J.

McPhaden, 1998: Primary productivity in the equatorial

Pacific during the onset of the 1997-98 El Nino.

AGU/ASLO Ocean Sciences Meeting, San Diego, CA.

Strutton, Peter G., Francisco P. Chavez and Michael J.

McPhaden, 1998: Primary productivity in the equatorial

Pacific during the onset of the 1997-98 El Nino. Pacific

Climate Workshop, Catalina Island.

Strutton, Peter G., Francisco P. Chavez and Michael J.

McPhaden, 1998: In situ measurements of primary

productivity in the equatorial Pacific during the 1997-98

El Niño. AGU Fall Meeting, San Francisco, CA.

Strutton, Peter, Francisco Chavez, David Foley, Brian

Schlining and Michael J. McPhaden, 1999: Remote

Sensing of Biological-Physical Coupling in the Equatorial

Pacific. ASLO Ocean Sciences Meeting, Santa Fe, NM.
SIMBIOS Project Annual Report

58

Chapter 8

High Frequency, Long Time Series Measurements from the

Bermuda Testbed Mooring in Support of SIMBIOS

Tommy Dickey, Laura Dobeck, David Sigurdson,

Sarah Zedler, Derek Manov and Xuri Yu

University of California at Santa Barbara, Santa Barbara, California

8.1 INTRODUCTION

It has been recognized that optical moorings are important

platforms for the validation of SeaWiFS (Mueller and Austin,

1992). It was recommended that optical moorings be

maintained in order to 1) provide long-term time series

comparisons between in situ and SeaWiFS measurements of

normalized water-leaving radiance (e.g., Gordon and Wang,

1994), 2) develop and test algorithms for pigment biomass and

phytoplankton primary productivity, and 3) provide long-term,

virtually continuous in situ observations which can be used to

determine and optimize the accuracy of derived satellite

products. These applications require the use of in situ

radiometers for long periods of time to evaluate and correct

for inherent satellite undersampling (aliasing and biasing) and

degradation of satellite color sensors (e.g., drifts as

experienced by CZCS).

The Bermuda Testbed Mooring program was initiated in

1994 at a site located about 80km southeast of Bermuda (31°

43' N, 64° 10' W) in waters of about 4530m depth (Dickey et

al., 1997, 1998a, 2000). In August 1997 (i.e., BTM

Deployment 8), with NASA's support, we started to provide

the SIMBIOS program with large volumes of high frequency,

long-term time-series bio-optical data from the BTM for

SeaWiFS satellite ocean color groundtruthing and algorithm

development. This NASA supported portion of the BTM

activity spanned three years and covered five BTM

deployments (No. 7 - 12). During these three years, the quality

of radiometric data has improved dramatically. Excellent

agreement between BTM moored data and both SeaWiFS and

nearby ship profile radiometric data (e.g., Figures 8.1 and 8.2)

demonstrate that technical advances in the moored optical

observations have reduced the major difficulties that moored

platforms face: biofouling and less frequent calibration.

8.2 RESEARCH ACTIVITIES

Key NASA supported BTM optical measurements have

included: surface downwelling spectral irradiance (7 • 's,

SeaWiFS matched: • = 412, 443, 490, 510, 555, 665, and

683nm;10nm bandwidth at half power) and subsurface

downwelling spectral irradiance and upwelling spectral

radiance (7 • 's for each, SeaWiFS matched). Data are

collected at 6Hz for 45sec every hour during daylight and at

midnight. In 1999, a 3-wavelength radiance sensor (• = 443,

490, and 555nm) at 7m and 7-wavelength radiance systems at

5 and 10m were deployed. These additional systems, with

similar sampling schemes, significantly improve accuracy of

the derived products such as water-leaving radiance (Lw) and

spectral diffuse attenuation (Kd) which need vertical gradients

of direct measurements for derivation. All the hourly averaged

data have been submitted to the SIMBIOS office along with

some high-resolution data with the original sampling rates. In

addition, a telemetry system was used to provide near realtime BTM radiometric data to the UCSB Ocean Physics

Laboratory for Deployments 11 and 12 (April - November

1999) and a software package was developed to make key data

available to the SIMBIOS Project on a daily basis.

Products derived from the measured radiometric

quantities include spectral attenuation coefficients, spectral

reflectance ratios, and spectral water-leaving radiances. These

data provide necessary links to and interpolation of

radiometric data for remotely sensed observations of ocean

color (e.g., SeaWiFS color imager), which can be used to

estimate biomass and primary productivity globally. Spectral

diffuse attenuation (Kd) at 490nm and water-leaving radiances

(Lw) at 6 wavelengths (• = 412, 443, 490, 510, 555 and

665nm) were submitted to the SIMBIOS office. Time series of

hourly averaged Lw at 412, 490 and 555nm and their

comparison with the Lw from Bermuda Bio-optical Project

(BBOP; e.g., Siegel et al. 1995) are shown in Figure 8.1 a and

b. Intercomparison of near-noon Lw between BTM and

SeaWiFS is shown in Figure 8.2.

The number of match-ups between BTM and SeaWiFS

Lw is limited by the availability of SeaWiFS data. In addition

to excellent agreement with both BBOP and SeaWiFS

products, the BTM provides large volumes of virtually

continuous data; continuity is certainly very important for

studies on high-frequency variability.
SIMBIOS Project Annual Report

59

0

1

2

3

(b) Comparison of BTM Lw and BBOP Lw

r 412nm 2=0.64, r2

95=0.29

0

1

2

Lw (uW/cm2/nm/sr)

r 490nm 2=0.64, r2

95=0.28

0

0.5 r 555nm 2

=0.63, r2

95=0.26

97 98 99 124 125 126 127 152 153 155 186 187 188 189 190 214 215

Julian day (1999)

100 120 140 160 180 200 220 240 260 280 300

0

10

20

Wind spd (m s−1)

(a) BTM Wind Speed and Water−leaving Radiance

100 120 140 160 180 200 220 240 260 280 300

0

2

4

412 nm

100 120 140 160 180 200 220 240 260 280 300

0

1

2

3

490 nm

Lw (uW/cm2/nm/sr)

100 120 140 160 180 200 220 240 260 280 300

0

0.5

555 nm

Julian Day (1999) April 1 May 1 June 1 July 1 Aug 1 Sept 1 Oct 1 Nov 1

Figure 8.1. (a) Time series of surface wind speed (m s-1) and water-leaving radiance Lw (•W cm-2 nm-1 sr-1) for three selected

wavelengths from BTM Deployments #11 and #12 (April 1 - November 6, 1999). Wavelengths are indicated in the upper-right

corner of each panel.
SIMBIOS Project Annual Report

60

0

1

2

3

Jday 92.5

5.1 m/s

0

1

2

3

Jday 94.5

1.6 m/s

0

1

2

3

Jday 96.5

Intercomparison of SeaWiFS L wand BTM Lw

9.1 m/s

0

1

2

3

Jday 100.5

10.1 m/s

0

1

2

3

Jday 101.5

7 m/s

0

1

2

3

Jday 102.5

10.1 m/s

0

1

2

3

Jday 105.5

6.2 m/s

0

1

2

3

Jday 107.5

4.5 m/s

0

1

2

3

Jday 112.5

2.5 m/s

0

1

2

3

Jday 116.5

2.8 m/s

0

1

2

3

Jday 125.5

Lw (μW/cm2/nm/sr)

2.5 m/s

0

1

2

3

Jday 127.5

1.1 m/s

0

1

2

3

Jday 129.5

1.8 m/s

0

1

2

3

Jday 130.5

0.6 m/s

0

1

2

3

Jday 131.5

2.1 m/s

0

1

2

3

Jday 134.5

5.9 m/s

0

1

2

3

Jday 140.5

5.3 m/s

0

1

2

3

Jday 141.5

5 m/s

0

1

2

3

Jday 142.5

4 m/s

0

1

2

3

Jday 145.5

4.3 m/s

0

1

2

3

Jday 152.5

5.1 m/s

0

1

2

3

Jday 153.5

2.9 m/s

0

1

2

3

Jday 156.5

3.9 m/s

0

1

2

3

Jday 158.5

3 m/s

400 500 600

0

1

2

3

Jday 160.5

1 m/s

400 500 600

0

1

2

3

Jday 161.5

2 m/s

400 500 600

0

1

2

3

Jday 163.5

3.9 m/s

400 500 600

0

1

2

3

Jday 165.5

Wavelength (nm)

5.8 m/s

400 500 600

0

1

2

3

Jday 171.5

1.7 m/s

400 500 600

0

1

2

3

Jday 172.5

2.7 m/s

Figure 8.1. (b) Intercomparison between BTM Lw and BBOP Lw. Only those days when BBOP data are available are chosen.

Circles are BTM data and asterixs are BBOP data. Vertical bars are 95% confidence intervals for hourly averaged BTM data.

Squared cross-correlation coefficients and 95% significance levels are shown in the upper-left corner of each panel.
SIMBIOS Project Annual Report

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0

1

2

3

Jday 173.5

5.2 m/s

0

1

2

3

Jday 180.5

2.6 m/s

0

1

2

3

Jday 182.5

Intercomparison of SeaWiFS L wand BTM Lw

4.7 m/s

0

1

2

3

Jday 183.5

2.3 m/s

0

1

2

3

Jday 185.5

2.1 m/s

0

1

2

3

Jday 187.5

3.3 m/s

0

1

2

3

Jday 192.5

1.7 m/s

0

1

2

3

Jday 193.5

5 m/s

0

1

2

3

Jday 194.5

3.8 m/s

0

1

2

3

Jday 201.5

4.8 m/s

0

1

2

3

Jday 216.4

Lw (μW/cm2/nm/sr)

3.9 m/s

0

1

2

3

Jday 221.5

4.9 m/s

0

1

2

3

Jday 227.5

2.8 m/s

0

1

2

3

Jday 229.4

1.3 m/s

0

1

2

3

Jday 231.5

1.9 m/s

0

1

2

3

Jday 232.5

1.1 m/s

0

1

2

3

Jday 233.4

1 m/s

0

1

2

3

Jday 238.4

1.8 m/s

0

1

2

3

Jday 239.4

3.9 m/s

0

1

2

3

Jday 241.5

3.6 m/s

0

1

2

3

Jday 242.5

1.7 m/s

0

1

2

3

Jday 243.5

2.4 m/s

0

1

2

3

Jday 247.4

6.4 m/s

0

1

2

3

Jday 249.4

6.5 m/s

400 500 600

0

1

2

3

Jday 250.5

3.9 m/s

400 500 600

0

1

2

3

Jday 254.5

5.9 m/s

400 500 600

0

1

2

3

Jday 256.4

5.2 m/s

400 500 600

0

1

2

3

Jday 265.4

Wavelength (nm)

4.4 m/s

400 500 600

0

1

2

3

Jday 269.5

0.9 m/s

400 500 600

0

1

2

3

Jday 283.5

5.5 m/s

Figures 8.2. Spectral water-leaving radiance Lw derived from the BTM radiometers and from the SeaWiFS ocean color satellite

for 60 days during Deployments #11 and #12 (April 1 - November 6, 1999). These panels show Lw values near noon on these

60 days (Julian day shown on the top of each panel). The circles indicate data from SeaWiFS and asterisks indicate BTM data.

Noontime (1-h average) wind speed is indicated and the upper-right corner shows time series of the incident shortwave

radiation for the day of interest. Cloudy days are evident.
SIMBIOS Project Annual Report

62

8.3 RESEARCH RESULTS

Research activities are two-fold: technical improvements

and scientific studies. Technically, moored optical platforms

face two major issues: less frequent sensor calibration and

biofouling. Continuous in situ calibration is not possible for

moored instruments; therefore, BTM radiometric data are

processed with pre-deployment and post-deployment factory

calibrations. To deal with biofouling, copper shutters were

recently installed adjacent to each optical window. The

windows are open only during sampling. This technique is

proving quite successful and extends the useful period of

sampling. In addition, these data are compared with available

concurrent BBOP shipboard profiling measurements and

SeaWiFS data. We have applied a series of quality control and

consistency tests, some of which were developed by Jay

O'Reilly (Maritorena and O'Reilly, 2000), to the BTM data.

Scientifically, the radiometric data from the BTM have

been used for a variety of studies. From these studies, several

papers have been published (e.g., Dickey et al, 1998, 2000;

Stramska and Dickey, 1998; McGillicuddy et al., 1998;

McNeil et al., 1999). We are presently exploring the effects of

wind speed, waves, solar altitudes and other atmospheric and

oceanic conditions on radiometric properties. In addition, we

have statistically analyzed intercomparison data between

BTM, SeaWiFS and BBOP Lw, quantifying effects and biases

arising from different undersampling methodologies by ships

and satellites.

8.4 CONCLUSIONS

We have demonstrated that moorings-of-opportunity,

such as the BTM, can provide large volumes of useful

radiometric measurements for groundtruthing and algorithm

development for ocean color satellite imagers. Further, it

appears that BTM data sets will be quite useful for developing

satellite correction algorithms for various environmental and

undersampling effects. The BTM is a mature platform

enabling the collection of large volumes of quality radiometric

data for use with ocean color satellites. The concept of

moorings-of-opportunity has been shown to be viable and in

many ways superior to more traditional approaches. The next

step is to implement optical measurements similar to those

developed for the BTM SIMBIOS project using moorings in

selected ocean regions as part of global ocean observatory

programs.

ACKNOWLEDGMENTS

Data used for comparing ship-based and BTM data were

shared by David Siegel and Margaret O'Brien. SeaWiFS data

were provided for comparisons by the SIMBIOS Office,

which also gave important input and suggestions for our study.

The Bermuda Testbed Mooring program is supported by the

NASA SIMBIOS program, the National Science Foundation,

the Office of Naval Research, the National Ocean Partnership

Program, and the University of California, Santa Barbara.

REFERENCES

Dickey, T.D., D. Frye, H.W. Jannasch, E. Boyle, and A.H.

Knap, 1997: Bermuda sensor system testbed, Sea Tech.,

81-86.

Dickey, T., D. Frye, H. Jannasch, E. Boyle, D. Manov et al.,

1998a: Initial results from the Bermuda Testbed Mooring

Program, Deep-Sea Res. I, 45, 771-794.

Dickey, T., D. Frye, J. McNeil, D. Manov, N. Nelson, D.

Sigurdson, H. Jannasch, D. Siegel, A. Michaels, and R.

Johnson, 1998b: Upper ocean temperature response to

Hurricane Felix as measured by the Bermuda Testbed

Mooring, Mon. Wea. Rev., 126, 1195-1201.

Dickey, T., S. Zedler, X. Yu, S. Doney, D. Frye, H. Jannasch,

D. Manov, D. Sigurdson, J.D. McNeil, L. Dobeck, T.

Gilboy, 2000: Physical and biogeochemical variability

from hours to years at the Bermuda Testbed Mooring:

June 1994 – March 1998. Deep-Sea Res., (accepted).

Gordon, W.R. and M. Wang, 1994: Retrieval of water-leaving

radiance and aerosol optical thickness over the oceans

with SeaWiFS: a preliminary algorithm, Appl. Optics, 33,

443-452.

Maritorena, S. and J. O'Reilly, 2000: Update on the

operational SeaWiFS chlorophyll a algorithm. In

SeaWiFS Postlaunch Calibration and Validation

Analyses, Part 2, NASA Technical Memorandum 1999-

206892, in press.

McGillicuddy, D.J., A.R. Robinson, D.A. Siegel, H.W.

Jannasch, R. Johnson, T.D. Dickey, J.D. McNeil, A.F.

Michaels, and A.H. Knap, 1998: New evidence for the

impact of mesoscale eddies on biogeochemical cycling in

the Sargasso Sea, Nature, 394, 263-266.

McNeil, J. D., H. Jannasch, T. Dickey, D. McGillicuddy, M.

Brzezinski, and C. M. Sakamoto, 1999: New chemical,

bio-optical, and physical observations of upper ocean

response to the passage of a mesoscale eddy, accepted, J.

Geophys. Res., 104, 15,537-15,548.

Mueller, J.L. and R.W. Austin, 1992: Ocean Optics Protocols

for SeaWiFS Validation, SeaWiFS Tech. Report Series,

NASA Technical Memorandum 104566, Goddard Space

Flight Center, Greenbelt, MD, 43pp.

Siegel, D.A., A.F. Michaels, J.C. Sorensen, M. O’Brien, and

M.A. Hammer, 1995: Seasonal variability of light
SIMBIOS Project Annual Report

63

availability and utilization in the Sargasso Sea, J.

Geophys. Res., 100, 8675 8713.

Stramska, M. and T. D. Dickey, 1998: Short term variability

of the underwater light field in the oligotrophic ocean in

response to surface waves and clouds. Deep-Sea Res. I,

45, 1393-1410.

This research was supported by the

SIMBIOS NASA contract # 97127

PEER REVIEWED PUBLICATIONS

Dickey, T. D., D. Frye, H. W. Jannasch, E. Boyle and A. H.

Knap, 1997: Bermuda sensor system testbed. Sea Tech.,,

81-86.

Dickey, T., D. Frye, H. Jannasch, E. Boyle, D. Manov, D.

Sigurdson, J. McNeil, M. Stramska, A. Michaels, N.

Nelson, D. Siegel, G. Chang, J. Wu, and A. Knap, 1998:

Initial results from the Bermuda Testbed Mooring

Program. Deep-Sea Res. I, 45, 771-794.

Dickey, T., D. Frye, J. McNeil, D. Manov, N. Nelson, D.

Sigurdson, H. Jannasch, D. Siegel, A. Michaels, and R.

Johnson, 1998: Upper ocean temperature response to

Hurricane Felix as measured by the Bermuda Testbed

Mooring. Mon. Wea. Rev., 126, 1195-1201.

Dickey, T., S. Zedler, X. Yu, S. Doney, D. Frye, H. Jannasch,

D. Manov, D. Sigurdson, J.D. McNeil, L. Dobeck, T.

Gilboy, 2000: Physical and biogeochemical variability

from hours to years at the Bermuda Testbed Mooring:

June 1994 – March 1998. Deep-Sea Res., in press.

Dickey, T., 2000: Instrumentation and new technologies,

Chapter 6 in the book, Oceans 2020: Science for Future

Needs, (in press).

Dickey, T., 2000: Sensors: inherent and apparent optical

properties, in Encyclopedia of Ocean Science, eds. J.

Steele, J. Thorpe, and K. Turekian, (in press).

Dickey, T., and P.G. Falkowski, 2000: Chapter 9. Solar energy

and its biological-physical interactions in the sea. The

Sea, (in press).

Glenn, S.M., W. Boicourt, B. Parker, and T.D. Dickey, 2000:

Operational observation networks for ports, a large

estuary, and an open shelf. Oceanography, 13, 12-23.

McGillicuddy, D. J., A. R. Robinson, D. A. Siegel, H. W.

Jannasch, R. Johnson, T. D. Dickey, J. D. McNeil, A. F.

Michaels, and A. H. Knap, 1998: New evidence for the

impact of mesoscale eddies on biogeochemical cycling in

the Sargasso Sea. Nature, 394, 263-266.

McNeil, J. D., H. Jannasch, T. Dickey, D. McGillicuddy, M.

Brzezinski, and C. M. Sakamoto, 1999: New chemical,

bio-optical, and physical observations of upper ocean

response to the passage of a mesoscale eddy, J. Geophys.

Res., 104, 15,537-15,548.

Stramska, M. and T. D. Dickey, 1998: Short term variability

of the underwater light field in the oligotrophic ocean in

response to surface waves and clouds. Deep-Sea Res. I,

45, 1393-1410.

PRESENTATIONS

Dickey, T., 1999: Recent Advances in Interdisciplinary

Technologies and Their Potential Utilization in Ocean

Observing Systems. Proceedings of the International

Symposium: The Ocean Observing System, St. Raphael,

France.

Dickey, T. D., X. Yu, L. Dobeck, S. Zedler, D. Sigurdson, and

D. Manov, 2000: Optimal Utilization of Remotely-Sensed

Color Data for Ocean Carbon Cycle Studies. AGU/ASLO

Ocean Sciences Meeting, San Antonio, Texas.

Dickey, T., Emerging interdisciplinary technologies and their

potential utilization in the Global Ocean Observing

System, 2000: Oceanology 2000, Brighton, UK.

Dickey, T., 2000: The Bermuda Testbed Mooring: A mooringof-opportunity for groundtruthing ocean color satellites,

Oceans from Space, Venice, Italy.

Griffiths, G., R. Davis, C. Eriksen, D. Frye, P. Marchand, and

T. Dickey, 1999: Towards new platform technology for

sustained observations. Proceedings of the International

Symposium: The Ocean Observing System, St. Raphael,

France.

Manov, et al., 1998: Design of bio-optical and physical

systems for moorings: Bermuda Testbed and Coastal

Mixing and Optics. AGU/ASLO, San Diego.
SIMBIOS Project Annual Report

64

McNeil ,et al., 1998: Bio-optical and chemical observations of

the response of the upper ocean to the passage of a

mesoscale eddy at the Bermuda Testbed Mooring.

AGU/ASLO, San Diego.

Send, U., R. Weller, S. Cunningham, C. Eriksen, T. Dickey,

M. Kawabe, R. Lukas, M. McCartney, S. Osterhuis, 1999:

Oceanographic Time-series observations. Proceedings of

the International Symposium: The Ocean Observing

System, St. Raphael, France.

Sigurdson, et al., 1998: Design of a near real-time telemetry

system for the Bermuda Testbed Mooring (BTM).

AGU/ASLO, San Diego.

Sigurdson, D. E., D. V. Manov, T. D. Dickey, D. E. Frye, and

J. Kemp, 2000: Results from the Near Real-Time

Telemetry System on board the Bermuda Testbed

Mooring (BTM). AGU/ASLO Ocean Sciences Meeting,

San Antonio, Texas.

Zedler, et al., 1998: Time series observations and model

simulations of Hurricane Felix, AGU/ASLO, San Diego.
SIMBIOS Project Annual Report

65

Chapter 9

Remote Sensing of Ocean Color in the Arctic: Algorithm

Development and Comparative Validation

Glenn F. Cota

Old Dominion University, Norfolk, Virginia

9.1 INTRODUCTION

The overall goal of this effort is to acquire a large biooptical database, encompassing most environmental variability

in the Arctic, to develop algorithms for phytoplankton biomass

and production and other optically active constituents. A large

suite of bio-optical and biogeochemical observations have

been collected in a variety of high latitude ecosystems at

different seasons.

The Ocean Research Consortium of the Arctic (ORCA)

(www.ccpo.odu.edu/~orca/) is a collaborative effort between

G.F. Cota of Old Dominion University (ODU), W.G. Harrison

and T. Platt of the Bedford Institute of Oceanography (BIO),

S. Sathyendranath of Dalhousie University and S. Saitoh of

Hokkaido University. ORCA has now conducted 12 cruises

and collected over 500 in-water optical profiles (Fig. 1A) plus

a variety of ancillary data. Observational suites typically

include apparent optical properties (AOPs), inherent optical

property (IOPs), and a variety of ancillary observations

including sun photometery, biogeochemical profiles and

productivity measurements. All quality-assured data have

been submitted to NASA's SeaBASS data archive. Our

algorithm development efforts address most of the potential

bio-optical data products for SeaWiFS, MODIS and GLI, and

provides validation for a specific areas of concern, i.e. high

latitudes and coastal waters.

9.2 RESEARCH ACTIVITIES

Two cruises on ships of opportunity were successfully

completed. The first cruise in May-June, 2000 was on the

CCG Hudson to the Labrador Sea. It was somewhat

abbreviated by ship’s mechanical problems, but still largely

successful. The second cruise in August, 2000 was on the

USCG Polar Star to the Chukchi and Beaufort Seas. This

icebreaker cruise was highly successful with substantial

collections of both AOPs and IOPs spanning a very wide

range of environmental conditions.

Research efforts have focussed on data acquisition,

processing and analysis, match-up analysis, and modeling and

synthesis for publication. Our Labrador Sea data sets played a

central role in NASA’s and NASDA’s validation efforts for

OCTS, and have facilitated other lines of research (Pickart et

al. 2000). Shareware PERL code has been developed to attach

SeaBASS headers for common types of optical observations

and is available on our web site. Additional ancillary data are

also being submitted as they become available from our lab

and collaborators.

Data sets have also been or are being made available to

other investigators including Bernie Walters for detection of

surface slicks with SAR, Jay O’Reilly for algorithm

development of OC4 (O’Reilly et al. 2000) and spectral 1324

indices, Bob Pickart for time series analysis and feature

identification in the Labrador Sea, planning of phase II field

activities for NSF’s Western Arctic Shelf Basin Interaction

program, and others. The data sets are being made widely

available via the web as our web site is expanded to include

optical data, value-added products, a variety of ancillary

information, and presentations. Cruise reports, station sheets,

photos of sky and sea state, instrument specifications,

calibration histories, etc. will be available.

9.3 RESEARCH RESULTS

High latitude ecosystems are often highly productive with

a large range for biomass, which must be considered in

algorithm development. Polar waters experience unique

environmental conditions, and their bio-optical properties

differ markedly from temperate data and models (e.g. Mitchell

1992, Sathyendranath et al. 2000). The current OC4V4

SeaWiFS algorithm shown in Figure 9.1 incorporates over half

of our Arctic data (O’Reilly et al. 2000), and certainly is an

improvement over previous SeaWiFS algorithms. However,

compared to our tuned linear (OC4L) Arctic algorithm

SeaWiFS OC4V4 underpredicts chlorophyll concentrations

>1.5X across most of the range (Fig. 9.1B). Polynomial

models (i.e. OC4P) may improve determination coefficients

slightly, but there are no compelling bio-physical reasons to

adopt them and they can be unreliable if extrapolated.

Regional differences are evident in our Arctic datasets (Fig.

9.1A), but comparisons are confounded by different and

limited ranges of biomass and environmental conditions.
SIMBIOS Project Annual Report

66

Taxon-specific algorithms are attractive because of

seasonal succession, regional dominance, and potential

differences in taxon-specific productivity, export, or carbon

cycling. Diatoms or prymnesiophytes often dominate polar

phytoplankton assemblages, but are their optical signatures

sufficiently distinct to discriminate between groups?

Prymnesiophytes dominated reflectances signatures in the

Labrador Sea (Fig. 9.2A), and had higher chlorophyll-specific

absorption than diatoms (Cota et al. 2000a, Sathyendranath et

al. 2000). Stations dominated by diatoms may have a distinct

bio-optical relationship (Fig. 9.2A). However, taxon-specific

algorithms seem premature given limited ranges, small sample

sizes, and overlapping reflectance ratios.

There is a large degree of similarity between most

algorithms developed at high latitude (Fig. 9.2B), where each

algorithm is plotted over the approximate range of

observations it was developed from. Our latest Arctic 2000

(Arc00) data extends the lower chlorophyll range, confirming

a few low observations from the Gulf of Alaska and Bering

Sea. Inclusion of these data changes the slope significantly

from other regions like the Labrador Sea (Cota et al. 2000a).

The latest Arctic OC4L lines now agrees better with the new

OC4V4 SeaWiFS global algorithm except at low chlorophyll

concentration (Figs. 9.1B & 9.2B). The most strikingly

different algorithm is Arrigo et al.’s (1998) D+P for diatoms

plus Phaeocystis (prymnesiophytes). Statistical differences

between the slopes or intercepts for their algorithm were not

reported, but their Table 4 and Figure 9 suggests their diatom,

Phaeocystis, D+P, and all combined algorithms were very

similar, perhaps indistinguishable. Mitchell’s (1992)

algorithms for the Arctic and Antarctic, Arrigo et al.’s (1998)

for cryptophytes from the Ross Sea, Deirrsen and Smith’s (in

press) for the Antarctic Peninsula region and our Labrador Sea

algorithm (not shown, Cota et al. 2000a) all display similar

slopes. The range(s) of data used to develop algorithms can

have a major influence on the slope of lines, and a broad range

of environmental conditions is critical to develop robust

algorithms. Mitchell’s (1992) algorithms utilized radiance

(not reflectance) ratios, slightly different spectral bands

(488/560 nm), and included chlorophyll plus phaeopigments

or “CZCS-pigment”. Phaeopigments typically have a very

minor influence on these relationships and should be excluded.

Remote sensing reflectance Rrs measurements made

above-water have obvious utility, but are not widely accepted.

Comparisons of select in-water and above-water Rrs ratios

reveal a strong relationship in Figure 9.3 explaining about

92% of the variability. The slope is lower than unity in this

limited data set. However, above-water reflectances appear to

be slightly higher than in-water values, and more work is

needed to clarify these differences and their variability.

Inherent optical properties hold the key to much of the

regional variability. Relatively large diatom cells in high

Arctic waters can be highly packaged (>90%) with very low

chlorophyll-specific absorption compared with lower latitude

ecosystems (Cota et al. 2000b). Specific absorption for

diatoms at Resolute (Cota et al. 2000b) were about half those

in the Labrador Sea (Cota et al. 2000a). Simulations with

HydroLight (Mobley 1994) employing low, variable

backscattering and the mean absorption spectra for

phytoplankton and soluble materials reproduce the measured

reflectance spectra except at lower biomass levels in the blue

and green regions. Backscattering appears to be about twice

as high at Resolute as in the Labrador Sea. Inclusion of

scattering by nonpigmented particles should resolve

discrepancies between observed and predicted reflectances.

Absorption due to soluble matter also varies regionally with

the highest values in our northernmost study site, the Canadian

Arctic Archipelago near Resolute Bay, but nowhere have we

found a good relationship with chlorophyll.

Several approaches for estimating primary production in

the Arctic have been evaluated, but more recent results are

being incorporated. Briefly, simple band-ratio algorithms

explain about 80% and 50% of the observed variability

between surface chlorophyll and integrated net daily

production, respectively. Several production models were also

tuned with our high latitude data for comparison Yoder's

(pers. comm., Howard and Yoder 1997) MODIS production

algorithm is a depth-integrated model employing satellite

chlorophyll, daily PAR, SST, and climatologies for MLD.

This model underestimated observed production with a slope

of 0.74 and explained 42% of the variance. This discrepancy

resulted primarily from melting ice, which made the observed

MLD 3-10 times shallower than climatological estimates.

Their estimated KPAR would also be considerably higher than

observed values at higher biomass levels. Another depthintegrated model by Behrenfeld and Falkowski (1997) utilizes

similar environmental inputs and a temperature-dependent Popt

derived from productivity profiles. Their model also

underpredicted production with a slope of 0.55, but explained

56% of the variance. Compound errors in satellite predicted

chlorophyll and production would be ~two to fourfold

underestimates. Performance deficiencies of various models

are being explored.

9.4 CONCLUSIONS

Outstanding ancillary observations from our lab and

international collaborators will be submitted as they become

available. Additional publications are being prepared. Further

improvements are being made to our web site.

ACKNOWLEDGEMENTS

Additional support was provided by Canada's Department

of Fisheries and Oceans, Natural Resources Canada, and

Japan’s NASDA. The author thanks W.G. Harrison, T. Platt,

S. Sathyendranath, V. Stuart, S. Saitoh, D.A. Ruble and J.

Wang.
SIMBIOS Project Annual Report

67

Figure 9.1. Tuned maximum-band chlorophyll algorithms for the Arctic and the OC4V4 SeaWiFS algorithm. A. Arctic

algorithms are linear (OC4L) and 4th order polynomial (OC4P). B. Chlorophyll retrievals from OC4L compared to OC4P and

SeaWiFS OC4V4.
SIMBIOS Project Annual Report

68

Figure 9.2. Taxon-specific algorithms and polar algorithm comparison. A. Two-band algorithms developed for the combined

data sets from the Labrador Sea (Lab-2L), pigment groups diagnostic of diatoms (D, large filled symbols) and prymnesiophytes

(P or Prymnes., large open symbols). Unidentified is U (small open symbols) and pigments were not available. B. The

“global” SeaWiFS OC4V4 (O’Reilly et al. 2000) is included for reference with our composite Arctic OC4L algorithm and

algorithms from the Antarctic peninsula region (Dierssen and Smith 2000), Ross Sea algorithms (Arrigo et al. 1998) for

diatoms plus Phaeocystis (D+P) and crytophytes (C), and pigment (chlorophyll plus phaeopigments) algorithms for both polar

regions (Mitchell 1992). Each algorithm is shown for the original range of values.
SIMBIOS Project Annual Report

69

Figure 9.3. Reflectance ratios from in-water (SPMR) and above-water (SAS) optical measurements.

REFERENCES

Arrigo, K. R., D. H. Robinson, D. L. Worthen, B. Schieber,

and M. P. Lizotte, 1998: Bio-optical properties of the

southwestern Ross Sea, J. Geophys. Res., 103, 21683-

21695.

Behrenfeld, M.J. and P.G. Falkowski, 1997: Photosynthetic

rates derived from satellite-based chlorophyll

concentration. Limnol Oceanogr. 42, 1-20.

Cota, G.F., W.G. Harrison, T. Platt, S. Sathyendranath and V.

Stuart, 2000a: Bio-optical properties of the Labrador Sea.

J. Geophys. Res. (submitted).

Cota, G.F., D.A. Ruble, and J. Wang, 2000b: Bio-optical

properties of high Canadian Arctic waters. J. Geophys.

Res. (submitted).

Dierssen, H. M. and Smith, R. C. 2000: Bio-optical Properties

and Remote Sensing Ocean Color Algorithms for

Antarctic Coastal Waters. J. Geophys. Res. 105, 26,301-

26,312.

Howard, K.L and J.A. Yoder, 1997: Contribution of the

subtropical Oceans to global primary production. in: Liu,

C.-T. (ed.) Space Remote Sensing of Subtropical Oceans,

pp. 157-168, Pergamon, London.

Mitchell,B.G. 1992: Predictive bio-optical relationships for

polar oceans and marginal ice zones. J. Mar. Syst. 3,91-

105.

Mobley, C. D. 1994: Light and Water: Radiative Transfer in

Natural Waters. Academic Press, San Diego. 592pp.

O'Reilly, J. E., Maritorena, S., Siegel, D. A., O'Brien, M. C.,

Toole, D., Mitchell, B. G., Kahru, M., Chavez, F. P.,

Strutton, P., Cota, G. F., Hooker, S. B., McClain, C. R.,

Carder, K. L., et al. 2000: Ocean color chlorophyll a

algorithms for SeaWiFS, OC2 and OC4: Version 4.

Hooker, S. B. and Firestone, E. R. NASA Tech. Memo.

2000-206892 , Vol. 11, p. 9-23.

Pickart, R.S., D.J. Torres, R.A. Clarke and G.F. Cota, 2000:

Hydrography of the Labrador Sea during active

convection. J. Phys. Oceanogr. (in revision).

Sathyendranath, S., G. Cota, V. Stuart, H. Maass and T. Platt,

2000: Remote Sensing of phytoplankton pigments: A

comparison of empirical and theoretical approaches. Intl.

J. Rem. Sens. (in press).

Sathyendranath, S., T. Platt, V. Stuart, H. Maass and G. Cota,

1999: Remote sensing of phytoplankton biomass and

primary production. Aquabiology 21,17-22.
SIMBIOS Project Annual Report

70

This research was supported by

the SIMBIOS NASA contract #97132

PEER REVIEWED PUBLICATIONS

Sathyendranath, S., G. Cota, V. Stuart, H. Maass and T. Platt,

2000: Remote Sensing of phytoplankton pigments: A

comparison of empirical and theoretical approaches. Intl.

J. Rem. Sens. (in press).

Sathyendranath, S., T. Platt, V. Stuart, H. Maass and G. Cota,

1999: Remote sensing of phytoplankton biomass and

primary production. Aquabiology 21, 17-22.

Submitted

Cota, G.F., W.G. Harrison, T. Platt, S. Sathyendranath and V.

Stuart, 2000a. Bio-optical properties of the Labrador Sea.

J. Geophys. Res. (submitted).

Cota, G.F., D.A. Ruble, and J. Wang, 2000b. Bio-optical

properties of high Canadian Arctic waters. J. Geophys.

Res. (submitted).

Pickart, R.S., D.J. Torres, R.A. Clarke and G.F. Cota, 2000.

Hydrography of the Labrador Sea during active

convection. J. Phys. Oceanogr. (in revision).

PRESENTATIONS

Cota, G.F., J.L. Anning, W.G. Harrison, T. Platt, D.A. Ruble,

& S. Sathyendranath, 1999; Chlorophyll algorithms for

the Labrador Sea. Global Line Imager ADEOS II

Meeting, Kyoto, Japan.

Cota, G.F. and L.R. Pomeroy, 2000: Arctic shelf-basin

production. Shelf-Basin Interactions pan Arctic meeting.

Callaway Gardens, Georgia.

Cota, G.F., 2000: Bio-optical oceanography and ocean color.

USCGC Polar Star.

Cota, G.F., D.A. Ruble, and J. Wang, 2000: Arctic bio-optical

properties: Distinct differences. Global Line Imager

ADEOS II meeting, Kanazawa, Japan.
SIMBIOS Project Annual Report

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Chapter 10

Satellite Ocean Color Validation Using Merchant Ships

Robert Frouin and David L. Cutchin,

Scripps Institution of Oceanography, University of California San Diego, California

Pierre-Yves Deschamps

Laboratoire d'Optique Atmospherique, Universite des Sciences et Technologies de Lille

Villeneuve d'Ascq, France

10.1 INTRODUCTION

A collaborative measurement program for evaluating

satellite-derived ocean color has been developed based on

ships of opportunity (merchant, oceanographic) and specific

instrumentation, the SIMBAD radiometer. The purpose of the

measurement program is to complement, in a cost-effective

way, dedicated evaluation experiments at sea, which are

expensive, cannot be carried out over the full range of

expected oceanic and atmospheric conditions, and generally

provide a few match-ups. Ships participate in the program on a

volunteer basis or at a very small cost, and measurement

procedures do not interfere with other ship activities.

The SIMBAD radiometer is a portable, easy-to-operate

instrument that measures the basic ocean color variables,

namely aerosol optical thickness and water-leaving radiance,

in typical spectral bands of ocean-color sensors, i.e., 443, 490,

560, 670, and 870 nm. Measuring these variables at the time

of satellite overpass is usually sufficient to verify satellitederived ocean color and to evaluate atmospheric correction

algorithms (Schwindling et al., 1998). Any ordinary crew can

learn quickly how to make measurements. Importantly, the

ship is not required to stop, making it possible to collect data

along regular routes traveled by merchant ships in the world’s

oceans.

Water-leaving radiance is measured by viewing the ocean

surface from the side of the ship lit by the sun, outside the sun

glint, at a nadir angle of 45 degrees and a relative azimuth

angle of 135 degrees. A vertical polarizer reduces skylight

reflected in the radiometer’s field of view. Aerosol optical

thickness is measured by viewing the sun, as done with classic

sun photometers. The same optics, with a 2.5 degree field of

view, and the same detectors with different gains are used in

ocean- and sun-viewing modes. The radiometric

measurements are made simultaneously in the instrument’s

five spectral bands, but the ocean and sun are viewed

sequentially. Incident solar irradiance at the surface, a variable

that must be known to normalize water-leaving radiance, is

not measured but calculated, which can be done accurately

when the sun is not obscured by clouds and cloud cover is less

than 30%.

To achieve adequate sampling, a series of ten radiometers

were built for use in two complementary networks, one

operated by the Scripps Institution of oceanography (SIO),

University of California San Diego, the other by the

Laboratoire d’Optique Atmosphérique (LOA), University of

Lille. Detailed information about the SIMBAD radiometer,

measurement procedures and protocols, calibration history,

processing software, and data are available at

http://polaris.ucsd.edu/~simbad/ (SIO network) and

http://www.loa.univ-lille1.fr/~simbad/ (LOA network).

10.2 RESEARCH ACTIVITIES

During the first year of the project efforts focused on

finding suitable merchant ships (e.g., ships with the bridge

forward), making arrangements with the ship owners and

captains, and training officers to make SIMBAD

measurements. Some of the selected ships, for example M/V

Micronesian Navigator (SIO network) and M/V Toucan (LOA

network) have proven reliable in providing data routinely.

Arrangements were also made to collect data during research

cruises, in particular the quarterly CalCOFI cruises off the

California coast. During the second and third year of the

project, as experience was gained and procedures became

refined, the collection of SIMBAD data sets intensified. Some

cruises allowed a verification of SIMBAD-measured waterleaving radiance. During October 1996-October 2000, 42

merchant ship voyages and research cruises were

accomplished by the SIO network, providing more than 2,000

data sets of concomitant aerosol optical thickness and waterleaving radiance. The location of the data sets is displayed in

Fig. 10.1. The major oceans were sampled, some data were

collected at high latitudes, but most of the data were collected

between latitudes of –40 and 40 degrees.

The SIMBAD data were used to verify the concept of the

SIMBAD radiometer, perform comparisons with other

instruments, calibrate vicariously the POLDER instrument,
SIMBIOS Project Annual Report

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and perform a first evaluation of SeaWiFS-derived waterleaving radiance. Activities also included verifying aerosol

models for ocean color remote sensing, characterizing an

extreme Asian dust event that reached the western United

States, and analyzing OCTS radiance data for aerosol remote

sensing. The main results of these activities are summarized in

the next section.

10.3 RESARCH RESULTS

Direct atmospheric transmittance and sky radiance data

collected during several years at the SIO pier in La Jolla were

analyzed (Swindling et al., 1998). The objectives were (1) to

verify whether the aerosol models selected for SeaWiFS are

adapted to ocean color remote sensing from space, and (2) to

identify what type of in situ atmospheric optics measurements

should be performed to verify atmospheric correction

algorithms. Aerosol optical thickness at 870 nm was generally

low in La Jolla, with most values below 0.1 after correction

for stratospheric aerosols. Two modes of variability

characterized by Angström coefficients of 1.2 and 0.5 and

corresponding to tropospheric and maritime models,

respectively, were identified in the measurements. The

SeaWiFS aerosol models allowed one to fit, within

measurement inaccuracies, the derived values of the Angström

coefficient and “pseudo” phase function (the product of single

scattering albedo and phase function), key atmospheric

correction variables. The study also showed that the “pseudo”

phase function could be derived from the Angström

coefficient, suggesting that sun photometer measurements at

the time of satellite overpass may be sufficient, in most cases,

to verify the atmospheric correction of ocean color.

The principle of the SIMBAD radiometer, in particular its

ability to reduce reflected skylight, was justified theoretically

and verified experimentally (Fougnie et al., 1999a).

Theoretical calculations showed that, for a nadir angle of 45

degrees and a relative azimuth angle between solar and

viewing directions of 135 degrees --the recommended

geometry, reflected skylight is reduced to typically 10-3 in

reflectance units at 443 nm (Fig. 10.2). This value represents 2

to 10% of the diffuse marine reflectance, the signal of interest.

Furthermore, the effects of surface roughness on skylight

reflection and, hence, uncertainties in sea state (wind speed)

are minimized (smaller error bars in Fig. 10.2). Taking into

account typical uncertainties in wind speed and geometry, the

residual reflected skylight is correctable to a few 10-4 in

reflectance units. For most oceanic waters, the resulting error

on the diffuse marine reflectance in the blue and green is less

than 1%. The theoretical results were verified experimentally

at the SIO pier, by viewing the ocean surface with a scanning

polarization radiometer (Fig. 10.3). The various angular and

spectral effects predicted by theory have been evidenced in the

measurements.

Figure 10.1. Location of the SIMBAD data sets collected by the SIO network.
SIMBIOS Project Annual Report

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0

0.005

0.01

0.015

0 30 60 90

Total

// Polarized

Reflectance

Viewing Zenith Angle (deg)

θ s

 = 47°, φ = 135° 

λ = 443 nm

Figure 10.2. Simulated ocean reflectance at 443 nm as a function of viewing zenith angle. Sun zenith angle is 47 degrees and

relative azimuth angle 135 degrees. The diffuse reflectance of the water body is equal to zero and the atmosphere only contains

molecules. Error bars represent minimum and maximum values for varied wind speed from 2 to 12.5 ms-1.

Since the water body polarizes incident, the measured

polarized radiance differs from the total radiance. For the

recommended geometry, the underwater scattering angle

varies only between 148 and 158 degrees, and the maximum

polarization factor varies between 0.83 and 0.92 (Fougnie et

al., 1999a). Fig. 10.4 shows, for typical phytoplankton

particles and a chlorophyll concentration of 0.1 mgm-3, the

ratio of parallel-polarized and total signals at 450 nm as a

function of viewing zenith angle. The relative azimuth angle is

50.6 degrees. Various sun zenith angles are considered in the

figure. For a sun not too close to zenith, one should be able to

correct the polarization effects to within 5% relative

inaccuracy, which compares with other errors, such as those

due to bi-directional effects and radiometric calibration. The

SIMBAD radiometer was evaluated at sea during various

experiments, in particular CalCOFI in the southern California

Bight and Aerosols-99 in the Atlantic and Indian Ocean. The

diffuse marine reflectance measured by the radiometer was

compared with that measured by an underwater MER optical

system. The agreement between the two types of

measurements is generally good, to within 10-15% (Fig. 10.5).

Another comparison was performed with a surface-floating

reflectance-meter in the Black Sea, and also showed good

agreement (Fig. 10.6). Aerosol optical thickness measured by

the SIMBAD radiometer also compared well with that

measured by sun photometers during the October-November

1996 CalCOFI cruise (Nakajima et al., 1999) and during the

Second Aerosol Characterization Experiment (ACE-II), as

illustrated in Fig. 10.7. A more extensive comparison of

aerosol optical thickness measurements made by SIMBAD

and Microtops radiometers, shadow-band radiometer, and a

lidar during Aerosols-99 further attested to the quality of the

SIMBAD measurements (Voss et al., 2001). SIMBAD data

were also used to evaluate airborne lidar measurements of

aerosol optical thickness over the Atlantic Ocean during a

European pollution outbreak (Flamant et al., 2000). The

SIMBAD radiometer proved useful to check vicariously the

radiometric sensitivity of the Polarization and Directionality of

the Earth’s Reflectance (POLDER) instrument onboard

ADEOS (Fougnie et al., 1999b). The satellite radiance was

compared with that computed for the same geometry using a

radiation-transfer model. SIMBAD measurements of aerosol

optical thickness and marine reflectance at the time of satellite

overpass were used as input to the model. The accuracy of the
SIMBIOS Project Annual Report

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0

0.01

0.02

0.03

0.04

0 30 60 90

SIO Pier

Total

// Polarized

Reflectance

Viewing zenith angle (deg)

θs

 = 55°, φ = 135°

λ = 443 nm

Figure 10.3. Measured ocean reflectance at 443 nm as a function of viewing zenith angle. Sun zenith angle is 55 degrees and

relative azimuth angle 135 degrees.

vicarious calibration coefficients was estimated to be better

than 3%. A large decrease of the POLDER instrument

response was found in the blue, confirming the results

previously obtained using alternative calibration techniques.

SIMBAD data were also used in an analysis of OCTS

radiance for aerosol remote sensing (Nakajima et al., 1999).

The study provided evidence that anthropogenic sulfate

aerosols in mid-latitudes and biomass-burning aerosols in the

sub-tropical regions are characterized by small particles (large

Angström coefficient), whereas mineral dust particles from

subtropical arid regions are characterized by small particles

(small Angström coefficient).

A study of an extreme Asian dust episode, which reached

the US Western Seaboard in April 1999, was also performed

(Tratt et al., 2001). This event was observed at several in situ

and remote sensing atmospheric instrument stations. Dramatic

reductions in boundary layer visibility were recorded and the

resultant peak backscatter coefficients exceeded uppertroposphere background conditions by at least two orders of

magnitude. At San Nicolas island the measured and modeled

aerosol optical thickness at 500 nm increased dramatically

from 0.15 on April 25 to 0.52 on April 26-27 (Fig. 10.8).

Volume size distribution on April 27 exhibited a prominent

coarse mode at 1-2 μm radius and single scattering albedo was

observed to increase from 0.90 in the blue to 0.93 in the near

infrared. Concurrent lidar observations tracked the evolution

of the plume vertical structure, which consisted of up to three

well-defined layers distributed throughout the free

troposphere.

SeaWiFS-derived marine reflectance was also evaluated

against SIMBAD measurements. Sixty-two high-quality

match-ups obtained in clear and turbid waters were analyzed

(Fig. 10.9). The scatter between the two types of data is fairly

large, but the agreement is generally good. A previous analysis

of fifteen match-ups had indicated higher SeaWiFS values in

the blue, but such overestimation is not apparent in Fig. 10.9,

which might be explained in part by the re-processing of

SeaWiFS data. Interpretation of the scatter in marine

reflectance will require examining aerosol optical thickness

retrievals and measurements. Thanks to the SIMBAD concept,

aerosol measurements corresponding to the marine reflectance

measurements are available for this analysis.
SIMBIOS Project Annual Report

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0.5

0.6

0.7

0.8

0.9

1

1.1

0 10 20 30 40 50 60 70

λ = 450 nm

 0Þ

15Þ

30Þ

45Þ

60Þ

75Þ

2 Ipar/(Ipar + Iper)

Viewing Zenith Angle (deg)

θ

s

φ= 50.6Þ, C = 0.1 mgm -3

Figure 10.4. Monte Carlo simulations of the ratio of parallel-polarized and total reflectance of the ocean at 450 nm as a

function of viewing zenith angle. Sun zenith angles are 0, 15, 30, 45, 60 and 75 degrees. The relative azimuth angle is 50.6

degrees. The ocean contains typical phytoplankton particles. Chlorophyll concentration is 0.1 mgm-3.

ACKNOWDEGMENTS

The authors wish to thank all the officers and scientists

that have collected SIMBAD data. They are grateful to J.

McPherson for developing the SIMBAD processing code, S.

Baily for adapting the code to PC and providing SeaWiFS

match-up data, A. Poteau for processing SIMBAD data and

maintaining the SIMBAD radiometers, and C. Pietras for

helpful discussions. This work has been supported by the

National Aeronautics and Space Administration under

Contract NAS-97135 (to R. Frouin), the Centre National

d’Etudes Spatiales, the Centre National de la Recherche

Scientifique, and the Région Nord-Pas de Calais.

REFERENCES

Flamant, C., J. Pelon, P. Chazette, V. Trouillet, P. Quinn, R.

Frouin, D. Bruneau, J.-F. Leon, T. Bates, J. Johnson, and

J. Livingston, 2000: Airborne lidar measurements of

aerosol spatial distribution and optical properties over the

Atlantic Ocean during an European pollution outbreak of

ACE-2. Tellus, 52B, 652-677.

Fougnie, B., R. Frouin, P. Lecomte, and P.-Y Deschamps,

1999a: Reduction of skylight reflection effects in the

above-water measurement of diffuse marine reflectance.

Appl. Optics, 38, 3844-3856.

Fougnie, B., P.-Y. Deschamps, and R. Frouin, 1999b:

Vicarious Calibration of the POLDER ocean color

spectral bands using in-situ measurements. IEEE Trans.

Geos. Rem. Sen., 37, 1567-1574.

Nakajima, T., A. Higurashi , K. Aoki, T. Endoh, FH.

Fukushima, M. Toratani, Y. Mitomi, B. G. Mitchell, and

R. Frouin, 1999: Early phase analysis of OCTS radiance

data for aerosol remote sensing. . IEEE Trans. Geo. Rem.

Sen., 37, 1575-1585.

Tratt, D. M., R. Frouin, and D. L. Westphal, 2001: The April

1998 Asian Dust event: a Southern California perspective.

J. Geophysical Res., in press.

 Schwindling, M. P.-Y. Deschamps, and R. Frouin, 1998:

Verification of aerosol models for satellite ocean color

remote sensing, J. Geophys. Res., 103, 24,919-24,935.
SIMBIOS Project Annual Report

76

Voss, K. J., E. Welton, P. Quinn, R. Frouin, M. Reynolds, and

M. Miller, 2001: Aerosol optical depth measurements

during the Aerosols-99 experiment. J. Geophys. Res., in

press.

0

0.01

0.02

0.03

0.04

0 0.01 0.02 0.03 0.04

Marine Reflectance, πLu(0 +)/Ed(0 +)

443 nm

490 nm

560 nm

670 nm

SIMBAD Radiometer

MER Radiometer

Figure 10.5. Comparison of marine reflectance measured by SIMBAD and MER radiometers during CalCOFI (southern

California bight) and Aerosols-99 (Atlantic and Indian Oceans).
SIMBIOS Project Annual Report

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0

0.004

0.008

0.012

0.016

0 0.004 0.008 0.012 0.016

Marine Reflectance, πLu(0+)/Ed(0+)

443 nm

490 nm

560 nm

670 nm

MHI Reflectance-meter

Simbad Radiometer

Black Sea

10/6/99-10/13/99

Figure 10.6. Comparison of marine reflectance measured by SIMBAD radiometer and Marine Hydrophysical Institute (MHI)

reflectance-meter during the October 1999 R/V Bilim cruise in the Black Sea.

0

0.05

0.1

0.15

0.2

0.25

0.3

0 0.05 0.1 0.15 0.2 0.25 0.3

Aerosol Optical Thickness

443 nm

490 nm

560 nm

670 nm

870 nm

AMES Tracking Sunphotometer

SIMBAD Hand-Held Sunphotometer

ACE-II

Figure 10.7. Comparison of aerosol optical thickness measured by SIMBAD radiometer and AMES tracking sun photometer

during ACE-II (northeast Atlantic Ocean).
SIMBIOS Project Annual Report

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0

0.1

0.2

0.3

0.4

0.5

0.6

18 20 22 24 26 28 30

San Nicolas Island

NAAPS

Aerosol Optical Thickness, 500 nm

Day (April 1998)

Figure 10.8. Time series of aerosol optical thickness at 500 nm at San Nicolas Island during April 18-May 1, 1998. Also shown

is simulated is optical depth (x0.25) of dust and sulfate from the NRL Aerosol Analysis and Prediction System (NAAPS).

0

0.01

0.02

0.03

0.04

0.05

0 0.01 0.02 0.03 0.04 0.05

Marine Reflectance, πLu(0 +)/Ed(0 +)

443 nm

490 nm

560 nm

670 nm

SeaWiFS

SIMBAD Radiometer

Figure 10.9. Comparison of marine reflectance derived by SeaWiFS and measured by the SIMBAD radiometer.
SIMBIOS Project Annual Report

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This research was supported by the

 SIMBIOS NASA contract # 97135

PEER REVIEWED PUBLICATIONS

Fougnie, B., P.-Y. Deschamps, and R. Frouin, 1999: Vicarious

Calibration of he POLDER ocean color spectral bands

using in situ measurements. IEEE Trans.Geos. Rem. Sen.,

37, 1567-1574.

Fougnie, B., R. Frouin, P. Lecomte, and P-Y. Deschamps,

1999: Reduction of kylight reflection effects in the abovewater measurement of marine iffuse reflectance. Appl.

Optics, 38, 3844-3856.

Nakajima, T., A. Higurashi, K. Aoki, T. Endoh, H.

Fukushima, M. Toratani,Y. Mitomi, B. G. Mitchell, and

R. Frouin, 1999: Early phase analysis of CTS radiance

data for Aerosol remote sensing. IEEE Trans. Geo. Rem.

Sen.,37, 1575-1585.

Schwindling, M. P.-Y. Deschamps, and R. Frouin, 1998:

Verification of erosol models for satellite ocean color

remote sensing, J. Geophys. Res.,103, 24,919-24,935.

PRESENTATIONS

Fougnie, B., P.-Y. Deschamps, R. Frouin, B. G. Mitchell,

1998: Measuring water-leaving radiance with a

polarization radiometer: theory and experimental

verification. EOS Trans., 79, No. 1, 99.

Frouin, R., P.-Y. Deschamps, B. G. Mitchell, and M. Kahru,

1998: The Normalized Difference Phytoplankton Index

for satellite ocean color applications. EOS Trans., 79, No.

1, 161.

Gross, L., R. Frouin, S. Thiria, and M. Crépon, 1998: Neural

networks to retrieve phytoplankton pigment concentration

from satellite-derived spectral ocean reflectance. EOS

Trans., 79, No. 1, 161.

Tratt, D. M., R. Frouin, and D. L. Westphal, 1999: A Southern

California perspective of the April, 1998 trans-Pacific

Asian dust event. Proceedings,10th Conference on

Coherent Laser Radar, Mt. Hood, Oregon, June 28 – July

2, 1999, 137-140.
SIMBIOS Project Annual Report

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Chapter 11

High Altitude Measurements of Radiance at High Spectral and

Spatial Resolution for SIMBIOS Sensor Calibration,Validation,

and Intercomparisons

Robert O. Green, Betina Pavri and Thomas G. Chrien

NASA, Jet Propulsion Laboratory, Pasadena, California

11.1 INTRODUCTION

The successful combination of data from different ocean

color sensors depends on the correct interpretation of signal

from each of these sensors. Ideally, the sensor measured

signals are calibrated to geophysical units of spectral radiance,

and sensor artifacts are removed and corrected. The calibration

process resamples the signal into a common radiometric data

space so that subsequent ocean color algorithms that are

applied to the data are based on physical processes and are

inherently sensor independent. The objective of this project is

to calibrate and validate the on orbit radiometric

characteristics of SeaWIFS with underflights of NASA’s

calibrated Airborne Visible/Infrared Imaging Spectrometer

(AVIRIS). This objective is feasible because AVIRIS

measures the same spectral range as SeaWIFS at higher

spectral resolution (Figure 11.1). In addition to satellite sensor

underflights, the AVIRIS project has supported comparison

and analysis of the radiometric calibration standards used for

AVIRIS and SeaWIFS. To date, both the OCTS and

SeaWIFS satellite sensors have been underflown by AVIRIS

with matching spectral, spatial, geometric, radiometric, and

temporal domains. The calibration and validation objective of

this project is pursued for the following reasons: (1)

Calibration is essential for the quantitative use of SeaWIFS

and other SIMBIOS sensor data; (2) Calibration in the

laboratory of spaceborne sensors is challenging; (3) Satellite

sensors are subjected aging on the ground and to trauma

during launch; (4) The Earth orbit environment is significantly

different than the laboratory calibration environment; (5)

Through years of effort AVIRIS has been demonstrated to be

well calibrated; and (6) AVIRIS can match the spectral and

spatial observation characteristics near the top of the

atmosphere at the time of SeaWIFS measurements.

11.2 RESEARCH ACTIVITIES

The approach taken for the SIMBIOS Project has been to:

(1) Determine the calibration accuracy of AVIRIS with high

confidence in the laboratory, in flight, and on the runway

before flight; (2) Underfly the SeaWIFS satellite sensor with

AVIRIS matching observation geometry and addressing

weather, satellite, aircraft, sensor, and location issues; (3)

Correct AVIRIS spectral image data to the top of the

atmosphere radiance; (4) Convolve AVIRIS spectral channels

to SeaWIFS bands; (5) Determine and extract matching areas

with correct observation geometry; (6) Compare and analyze

the matchup data and repeat acquisitions for monitoring.

Matching the location, time, and illumination/observation

angles of SeaWiFS is critical for comparison of radiometric

data. The calibration of the SeaWiFS sensor presented special

challenges due to the geometry of the sensor's observations.

SeaWiFS scans cross-track at an angle 20° behind the satellite

to avoid sunglint in the field of view. AVIRIS has a 15° scan

angle. In order to match the >20° view angle of SeaWIFS, the

AVIRIS ER-2 platform is banked at ~20°. This continuous

bank results in a circular "flightline".

Conversion of AVIRIS data to top-of-the-atmosphere

radiance is achieved through the use of the MODTRAN

atmospheric modeling package. The stability and repeatability

of AVIRIS calibration is validated through a series of inflight

calibration experiments. With pre- and post-flight calibrations

of AVIRIS, coupled with the on-board calibrator, calibration

accuracy of better than 2% spectral, 3% radiometric and 3%

spatial have been achieved.

11.3 RESEARCH RESULTS

AVIRIS data have been measured for the SIMBIOS

Project on 970520, 971002, 990807, and 991001. Results

from 1997 have been presented elsewhere, and the 991001

analysis is not yet complete, so only the 990807 results will be

discussed here. On 7 August 1999, AVIRIS successfully

underflew the SeaWIFS sensor off the east coast of the United

States. AVIRIS is typically flown in two circles during the

time of a SeaWIFS overpass, in order to span the time of the

satellite overpass and ensure that some AVIRIS
SIMBIOS Project Annual Report

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0

1

2

3

4

5

6

7

370 420 470 520 570 620 670 720 770 820 870 920 970

Wavelength (nm)

Ocean Radiance Spectrum

SeaWiFS Visible Near-Infrared Band

AVIRIS Spectral Channels (224 from 370 to 2500 nm

0

1

0

1

ROG970110

Solar Absorptions

H2O

H2O

H2O

H2O

O2

Ozone Absorption

Figure 11.1. The eight SeaWIFS bands and the AVIRIS spectrally contiguous channels in the region from 370 to 1000 nm. A

high spectral resolution modeled plot of the typical upwelling spectral radiance for an ocean target is shown in blue.

Figure 11.2. AVIRIS (left) and SeaWiFS (right) images of same region, taken contemporaneously. Regions of matching sun

angle and look angle are shown. Pixels matching to within 5° are shown in red shading, while pixels matching to within 2° are

marked with green.
SIMBIOS Project Annual Report

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0

1

2

3

4

5

6

7

8

9

12345678

band

-0.12

-0.1

-0.08

-0.06

-0.04

-0.02

0

0.02

0.04

0.06

0.08

0.1

0.12

fractional difference

aviris 2°

aviris 5°

seawifs 2°

seawifs 5°

fractional difference: 2°

fractional difference: 5°

Figure 11.3. A plot of the SeaWIFS and AVIRIS radiance extracted for the overlapping observation geometry. The fractional

differences are also plotted.

0

1

2

3

4

5

6

7

8

9

10

400 500 600 700 800 900 1000

wavelength (nm)

LXR band transmission

AVIRIS standard radiance

product

Figure 11.4. AVIRIS 1999 spectral radiometric calibration standard convolved and integrated to the Landsat Transfer

Radiometer (LXR) bands.
SIMBIOS Project Annual Report

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data is taken during the acquisition window. The data from the

flight are processed to calibrated radiance. Next, the data are

corrected to top of the atmosphere radiance using the

MODTRAN atmospheric model, supplimented with TOMS

ozone mapper data. The AVIRIS data are then convolved to

the reported SeaWiFS spectral response functions. To

compare SeaWIFS and AVIRIS data, the data sets must be

warped into the same geometric space, so that direct

comparisons are available between pixels which match in

space, time, illumination angle, and observation angle. Joe

Boardman of AIG developed algorithms and software to

enable us to use IDL/ENVI software to warp and analyze the

unusual circular flightlines, and to produce observation and

illumination angle from the AVIRIS navigation data. Using

this new software, the datasets are co-registered. With the two

data sets are projected into the same geometric space, the

SeaWIFS and AVIRIS regions (Figure 11.2) with overlapping

observation geometry can be selected, and the resulting

radiance compared (Figure 11.3). The results for the 990807

AVIRIS underflight are presented in Table 1. Two cases are

presented: matching illumination/observation angles to within

2°, and matching geometry to within 5°. Average radiance for

matching geometry regions are shown, followed by the

fractional differences between the AVIRIS and SeaWIFSderived radiance. Agreement within 10% is achieved, with the

exception of band 6. The magnitude of this discrepancy is

larger than expected and can not be explained purely by

AVIRIS radiometric uncertainty: The Landsat Transfer

Radiometer (LXR) was able to view the AVIRIS radiometric

calibration standard in June 1999. Analysis of the cross

comparison of the LXR and AVIRIS 1999 radiometric

standard was completed with good stability before and after

the measurements. Results of this analysis are given in Figure

11.4. The LXR measurements show a maximum discrepancy

of 2.5% between LXR measured and predicted radiance based

on LXR reported spectral response functions and AVIRIS

standard radiance. The results with the LXR are consistent

with the expected uncertainty of 2% to 3% of the AVIRIS

1999 radiometric calibration standard in this spectral region.

11.4 CONCLUSIONS

Analysis of these data show radiometric calibration

agreement ranging from 2% to 12% across the eight SeaWIFS

bands. This discrepancy can not simply be explained by

radiometric uncertainty within the AVIRIS calibration.

Further, they are inconsistent with the results from the Landsat

Transfer Radiometer experiment which show that the AVIRIS

1999 radiometric calibration standard agrees with the Goddard

Space Flight Center LXR at the 2-3% level. Analysis of the

SeaWiFS comparison is continuing in an attempt to

understand the disagereement. Drifting SeaWiFS filter

functions, a systematic error in SeaWiFS reported radiance,

and the AVIRIS technique for convolving AVIRIS data with

the SeaWiFS filter functions are all being considered as

possible sources of the observed discrepancy. Several

successful underflights of SeaWiFS were completed by

AVIRIS in 2000, but analysis has been slowed by the

reprocessing of the SeaWiFS data and a change in SeaWiFS

L1B data format. Analysis of this data will proceed over the

next several months. Comparison of these new results with

those collected in the previous experiments will provide

insight into the repeatability of the SeaWiFS sensor

calibration.

ACKNOWLEDGMENTS

This research was carried out at the Jet Propulsion

Laboratory / California Institute of Technology, Pasadena,

California, under contract with the National Aeronautics and

Space Administration. This research was funded as part of the

SIMBIOS Project at the GSFC/NASA.

This research was supported by the

SIMBIOS NASA interagency agreement

PRESENTATION

Green, R.O., B. Pavri, and M. Shimada, 1998: On-Orbit

Calibration of ADEOS OCTS with AVIRIS Underflight,

Proc. Seventh Airborne Earth Science Workshop, Jet

Propulsion Laboratory, JPL Pub. 97-21, Vol. 1, pp 205-

212.

Green, R. O., B. Pavri, J. Faust, O. Williams, and C. Chovit,

1998: Inflight Validation of AVIRIS Calibration in 1996

and 1997. Summaries of the Seventh JPL Airborne Earth

Science Workshop, Vol. 1.

Oaku, H., M. Shimada, and R. O. Green, 1998: OCTS

Absolute Calibration Using AVIRIS. IGARSS, Seattle.

Pavri, B., T. G. Chrien, and R. O. Green, 1999:

Intercomparison of Standards for Radiometric

Calibration. Presented at the Eighth JPL AVIRIS Science

and Applications Workshop.

Shimada, M., H. Oaku, Y. Mitomi, H. Murakami et al. 1999:

Calibration of the Ocean Color and Temperature Scanner.

IEEE Geoscience and Remote Sensing, a special issue of

ADEOS.
SIMBIOS Project Annual Report

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Chapter 12

Merging Ocean Color Data from Multiple Missions

Watson W. Gregg

NASA/Goddard Space Flight Center, Greenbelt, Maryland

12.1 INTRODUCTION

Oceanic phytoplankton may play an important role in the

cycling of carbon on the Earth, through the uptake of carbon

dioxide in the process of photosynthesis. Although they are

ubiquitous in the global oceans, their abundances and

dynamics are difficult to estimate, primarily due to the vast

spatial extent of the oceans and the short time scales over

which their abundances can change. Consequently, the effects

of oceanic phytoplankton on biogeochemical cycling, climate

change, and fisheries are not well known.

In response to the potential importance of phytoplankton

in the global carbon cycle and the lack of comprehensive data,

the National Aeronautics and Space Administration (NASA)

and the international community have established high priority

satellite missions designed to acquire and produce high quality

ocean color data. Seven of the missions are routine global

observational missions: the Ocean Color and Temperature

Sensor (OCTS), the Polarization and Directionality of the

Earth's Reflectances sensor (POLDER), Sea-viewing Wide

Field-of-view Sensor (SeaWiFS), Moderate Resolution

Imaging Spectrometer-AM (MODIS-AM), Medium

Resolution Imaging Spectrometer (MERIS), Global Imager

(GLI), and MODIS-PM. In addition, there are several other

missions capable of providing ocean color data on smaller

scales. Most of these missions contain the spectral band

complement considered necessary to derive oceanic pigment

concentrations (i.e., phytoplankton abundance) and other

related parameters. Many contain additional bands that can

provide important ancillary information about the optical and

biological state of the oceans.

Any individual ocean color mission is limited in ocean

coverage due to sun glint and clouds. For example, one of the

first proposed missions, the Sea-viewing Wide Field-of-view

Sensor (SeaWiFS), can provide about 45% coverage of the

global ocean in four days and only about 15% in one day

(Gregg and Patt, 1993).

Objectives

We propose to investigate, develop, and test algorithms

for merging ocean color data from multiple missions. We

seek general algorithms that are applicable to any retrieved

ocean color data products, and that maximize the amount of

information available in the combination of data from multiple

missions. Most importantly, we will investigate merging

methods that produce the most complete coverage in the

smallest amount of time, nominally, global daily coverage.

We will also assess the ability to produce fuller coverage in

larger time increments, including 4-day, 8-day (weekly), and

monthly. We intend to develop methods that are not missionspecific, but take advantage of the unique characteristics of the

missions as much as possible.

12.2 RESEARCH ACTIVITIES

Work has focused on 1) defining the problems and

opportunities provided by the existence of multiple sensors, 2)

analysis of past sensors to provide insights into the

characteristics, drawbacks, and advantages of individual

sensor responses in the context of merging, and 3) analysis,

development, and testing of candidate merger algorithms.

Defining Problems and Opportunities of Multiple Missions

• First we assessed the global coverage improvements

possible by the 6 global missions. These results show that

significant scientific advantages can accrue from assembling

and merging data from the multiple satellite platforms

proposed for ocean color in the next decade. The principal

advantages are increased ocean coverage in less time, and

new observations of the daily dynamics of phytoplankton

abundances, resulting from different observation times of

co-located ocean areas. Data from three satellites can

increase ocean coverage by 58% for one day, and 45% for

four days. Additional satellites produce diminishing returns,

however. This latter point is not necessarily an adverse

finding, since each mission has a limited life expectancy and

in-flight problems are, unfortunately, still not rare in the

satellite business. Using observations from pairs of

missions, as much as 14 hour time differences at co-located

points can be realistically achieved at high latitudes, even

considering the distribution of land masses and ice cover.

Smaller time differences are observed at lower latitudes, but

5 to 7 hour differences are still available. Furthermore,

massive numbers of these co-located pairs are available,

suggesting that routine scientific studies of diel

phytoplankton variability can be supported. These results
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were published in the IEEE Transactions on Geoscience and

Remote Sensing (Gregg et al., 1998).

• More detailed analyses emphasized seasonal and regional

coverage improvements and was limited to the

SeaWiFS/MODIS combination, since these are the two

missions planned for launch next. The results showed that

the launch of EOS AM-1 provides an opportunity to

potentially improve ocean color observations by combining

data from the SeaWiFS and MODIS missions. The sensors

have different scanning characteristics and are flying on

different platforms in different orbits. The results suggested

that very large improvements in coverage frequency (daily

to four-day) can be obtained by combining data from both

sensors: 40-47% increases in global coverage over SeaWiFS

alone in one day, and > 100% in areas near the solar

declination. Four-day increases are slightly smaller for

global coverage, 29-35%, but meridional percent increases

are similar to the one-day case. These differences are due to

reduced sun glint contamination obtained by tilting

(SeaWiFS), and scanning away from the maximum glint

region (MODIS), due to its 10:30 AM equator crossing

time, and due to the large scan width of MODIS. The

results show that SeaWiFS and MODIS are very

complementary ocean color missions that can provide more

complete observations of ocean processes at high frequency

if data are combined. These results were published in the

IEEE Transactions on Geoscience and Remote Sensing

(Gregg and Woodward, 1998). A more detailed paper with

more fully defined results was published as a NASA

Technical Memorandum (Woodward and Gregg, 1998).

• Assessed potential capability for improving ocean color

observations by selecting complementary mission orbits.

Considering that observations are severely hampered by

cloud cover and sun glint, a possibility exists for using

multiple missions to improve the coverage of the oceans in

shorter time scales. In fact, only about 10-18% of the

oceans are observed in a single day, even by so-called

global observational missions, due to these two ocean color

contaminants. Analysis of a 7-day period of cloud cover

from the International Satellite Cloud Climatology Project

(ISCCP), show that 12% new surface area is available for

viewing each day. This translates to an increase of about

0.5% per hour of separation in viewing times. Thus if the

ocean could be viewed by 2 different satellites 4 hours apart,

2% more ocean area could be observed. This represents a

coverage increase of about 13%. If 2 satellites were placed

in Earth orbit with equator crossing times 4 hours apart, the

improvement in coverage by these 2 satellites over a single

one is about 60%.

• Furthermore, by managing the orbits of the 2 satellites, nearly

complete sun glint avoidance can be achieved, further

improving the ocean coverage. The best improvements can

be made by 2 satellites in the same node, whose orbital

positions are adjusted to view the sun glint contaminated

areas of the other. If scan edges are useful for quantitative

ocean color observations (the validity of which is unknown

at this time), then only 2 polar-orbiting satellites are

necessary. If not, a third satellite is necessary, preferably in

a low inclination orbit where losses in coverage in the

tropics by the polar orbiters can be best compensated (polar

orbiters overlap coverage at high latitudes, and the scan

edges are necessary only near the equator). However, the

best configuration is 3 geostationary satellites, which

provide complete global coverage routinely with a viewing

time separation that can maximize cloud and sun glint

avoidance, with a single polar orbiter to provide high

latitude coverage. This option is considered expensive at

the present time, however.

Analysis of Past Sensors

Investigations with OCTS began before the failure of

ADEOS, in an effort to characterize its ocean color data for use

with a merging activity with SeaWiFS. The loss of data from the

sensor precludes its use in merging, but the similarity of some

aspects of sensor design with MODIS suggests that significant

understanding of the capabilities and deficiencies of MODIS

data can be facilitated through a thorough analysis of OCTS

data. In fact, we have found this to be true. Initial observations

just after failure of OCTS suggested 6 problem areas: band

registration, image striping, cloud noise, navigation, calibration,

and bright target response (Gregg, 1997). Analysis of the first

three led to solutions that improved imagery substantially

(Gregg, 1998; Gregg, 1999). Particularly the methods for

reducing image striping is a useful method for a problem

expected to occur in MODIS imagery. Analysis of geolocation,

radiometric stability and accuracy, and bright target responses

were characterized (Gregg et al., 1999). The results here also

have implications for MODIS.

Analysis of OCTS imagery indicated three areas of

impairment for quantitative scientific research applications: 1)

band misalignments, 2) image striping, and 3) image noise.

These impairments are caused by 1) band offsets in the sensor

design, 2) detector radiometric response variability, and 3)

cloud contamination (primarily) and the band offset design

(partially), respectively. The band offset design has

potentially serious implication for ocean color research, given

the presence of small scale variability, the requirement of

multiple bands to produce ocean color geophysical outputs,

and the sensitivity of the algorithms. A nearest neighbor

band-to-band co-registration method produced the best results,

with the least image noise, best cloud identification potential,

and most accurate depiction of the actual full spectral suite at a

given location. The offset design, however, still produced

inconsistencies in a spectral understanding of each OCTS

IFOV, and therefore provided a limitation on the sensor's

ultimate capability. We found it effective to handle image

striping by linearizing detector responses to the extent

possible, and then applying a median filter to a quasi-
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chlorophyll product to smooth out the individual detector

responses (Fig. 12.1). Location of the total radiances used to

produce the median filtered quasi-chlorophyll were retained

and enable smoothing of the total radiances, after which a

straightforward application of atmospheric correction can be

performed. The net effect of both band co-registration and

image striping reduction was to reduce the actual spatial

resolution from < 1 km to about 5 km. A multi-step method

for identifying and removing cloud effects was found to be a

reasonably effective method.

The OCTS archive collected by NOAA and NASA over

the US East Coast and Gulf of Mexico appeared to contain a

geometric offset of 4 to 5 pixels in the along-track direction.

Analyses of scan edges indicated that an adjustment to the tilt

produces the most reliable agreement to a high resolution land

data file. Analysis using an island matching algorithm

confirmed that an adjustment to the tilt reduces the along-track

bias: from about 5 pixels to 1.5 pixels for the tilted segment of

operations Mar. 18, 1997 to end of mission, and from 2.6 to

less than 0.3 pixels for the nadir pointing operations for Dec.

15, 1996 to Mar. 18, 1997.

OCTS appeared to exhibit near field scatter effects in its

imagery (Fig. 12.2). These effects were substantial in

magnitude, and had a spectral character, but were limited to

approximately the dimensions of the focal plane (30 pixels

wide by 10 pixels long). Not all bright targets exhibited these

near field effects, nor did the effects always last the width of

the focal plane. Most land features in the data set did not

appear to produce effects as large as clouds, which was

consistent with their relative brightness. Far field scatter

effects were difficult to demonstrate conclusively, since haze

or sub-saturating clouds could be ruled out. However, large

cloud features nearly always produced successively decreasing

reflectance far from their locations, and extending in all

directions (Fig. 12.3). The effects also appeared to be nonspectral, which conformed to the effects of far field scatter.

However, these effects, if they were in fact sensor-derived,

were not large although long lasting, and did not necessarily

produce adverse effects on ocean chlorophyll estimates

because of the error-correcting nature of the atmospheric

correction algorithms. Exceptions may occur in the presence

of large amounts of tropospheric aerosols, whose scattering

properties differ greatly from those of far field scatter and subsaturating clouds. In any event, similar problems, e.g., subsaturating clouds and thick haze, are encountered by all ocean

color sensors, and probably do not have a large impact on the

derivation of ocean properties.

Figure 12.1. OCTS chlorophyll image of May 5, 1997 in the US Mid-Atlantic Bight, showing the reduction in image striping effects

from uncorrected (a) to full striping corrections with a median filter applied (b). The reduction in image striping is dramatic, and the

reduction in spatial resolution is small. Chlorophyll is logarithmically scaled for display, according to C(scaled) =

4{[log10(C)+2]/0.012}. Thus, 128 scaled chlorophyll counts = 0.024 mg m-3, 256 counts = 0.059 mg m-3, 384 counts = 0.14 mg m-3,

512 counts = 0.34 mg m-3, 640 counts = 0.83 mg m-3, 768 counts = 2.0 mg m-3, and 896 counts = 4.9 mg m-3.
SIMBIOS Project Annual Report

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However, these effects could impact aerosol analyses over the

oceans. OCTS aerosol bands exhibited substantial short term

variability, producing daily differences in aerosol reflectance

ratios of 5 to >10%. Determination of longer term trends in the

stability of the aerosol bands was inconclusive, because of the

apparent long-term signal was much smaller than the short term

variability, and because a complete seasonal cycle was not

available due to the untimely loss of the mission after 9 months.

Figure 12.2 A transect through a north-south oriented cloud quantifies the apparent

effects of bright targets on OCTS. The reflectance ratio of Band 6 to Band 8

increases as the scan approaches the cloud from the west, producing an elevated

ratio to about 1.0 from a background value of 0.6 (these data utilize the pre-launch

calibration of OCTS, and are not expected to produce a realistic ratio). A

depression following the cloud produces the shadow that is apparent in the image.

These Band 6 and 8 effects are due to different spectral responses of OCTS to

bright targets due to the placement of the bands on the focal plane. Band 6 has a

nearly symmetric response on both sides of the cloud since it in nearly on the

optical axis. Band 8 exhibits a pronounced lingering effect on the east side of the

cloud. The combination of these two effects produce the brightening/darkening

effects seen in Fig. 3 when taken as a ratio. Bands 4 and 5 are at opposite ends of

the focal plane, and exhibit expected behavior given their relative positions. Band

5 Rayleigh-corrected reflectance has been increased by 0.0057 to emphasize its

effects relative to Band 4. Note that the major effects of near field scatter are

limited to about 30 pixels.
SIMBIOS Project Annual Report

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Figure 12.3. OCTS image of Florida and the eastern Gulf of Mexico on Jan. 24,

1997. Top: Band 8 aerosol reflectance. Bottom: Reflectance ratio of Band 6 to

Band 8. The cloud bank in the Northern Gulf of Mexico produces bright target

effects discussed before, but also appear to exhibit effects exceeding the 30 x 10

pixel dimensions of the focal plane. This may be due to far field scatter, which is

less intense than near field scatter but may affect observations at quite large

distances from bright targets. A key feature of far field scatter is its non-spectral

nature. A concurrent view of the reflectance ratio shows no apparent effects from

the cloud feature, suggesting spectral independence.

Atmospheric correction appeared to reduce the magnitude

of this short term variability when observing normalized waterleaving radiances, which exhibited daily variability of only about

1-5%. Seasonal variability also appeared to exist, although

observations in other areas of the oceans are necessary to

conform that these are not sensor effects. Similarly, long-term

trends of the sensitivity of OCTS Bands 1-5 were inconclusive

because of these daily and seasonal effects, which exceeded the

apparent mission lifetime effect. Comparison of calibration

adjustment factors derived from 6 in-situ data sources with those

obtained from an independent in-situ data set (Shimada et al.,

1998) showed good agreement in the bands used to evaluate the

optical state of water (Bands 1-5). Maximum differences in

these bands were < 4%, and differences 1% or less were

observed in Bands 1 and 2. Aerosol bands differed up to 5% in

Bands 6 and 7 and 11% in Band 8, but were attributed to a

measurement of Band 8 by an aircraft underflight by Shimada et

al. (1998) , while Band 8 was assumed unadjusted from its preflight value here, due to our inability to evaluate it by any other

means. However, the relative adjustments to Band 7 used here

produced ε(765,865) values in open ocean near 1, which are

expected under most conditions, as opposed to about 1.15 using
SIMBIOS Project Annual Report

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the Shimada corrections, which is somewhat higher than

expected.

Analysis and Development of Merging Algorithms

Efforts have begun on developing merging algorithms.

We are investigating 4 possible approaches: 1) simple

splicing/averaging, where data from 2 or more satellites are

averaged where they coexist at grid points, and use of a single

satellite in gaps where only one exists, 2) subjective analysis,

where specific dependences and deficiencies are identified

using knowledge about sensor environmental conditions and

co-located observations are merged using different weighting

functions for the sensors, 3) the Conditional Relaxation

Analysis Method (CRAM), where the best data are selected as

interior boundary conditions into a merged set using Poisson's

equation, and 4) optimal interpolation, where merging occurs

by weighting individual sensor data to minimize spatial

covariance function. All forms have been implemented in

software, and analysis of conceptual strengths and weaknesses

has completed. Preliminary analysis of SeaWiFS and MODIS

provided insight into operational aspects of the algorithms, but

refined MODIS flight data are necessary to refine these

conclusions.

REFERENCES

Gregg, W.W. and F.S. Patt, 1994. Assessment of tilt

capability for spaceborne global ocean color sensors.

IEEE Trans. Geosci. Remote Sens. 32, 866-877.

Gregg, W.W., F.S. Patt, and R.H. Woodward, 1997.

Development of a simulated data set for the SeaWiFS

mission. IEEE Trans. Geosci. Remote Sens., 35, 421-435.

Reynolds, R.W., 1988. A real-time global sea surface

temperature analysis. J. Clim., 1, 75-86.

Shimada, M. and others, 1998. Calibration and validation of the

ocean color version-3 product from ADEOS OCTS. J.

Oceanogr., 54, 401-416.

This research was supported by the

SIMBIOS NRA

PEER REVIEWED PUBLICATIONS

Gregg, Watson W., F.S. Patt and W.E. Esaias 1999: Initial

Analysis of Ocean Color Data from the Ocean Color and

Temperature Scanner. II. Geometric and Radiometric

Analysis, Applied Optics, 38, 5692-5702.

Gregg, Watson W., 1999: Initial Analysis of Ocean Color Data

from OCTS. I. Imagery Analysis, Applied Optics, 38, 476-

485.

Gregg , Watson W. and R.H. Woodward 1998: Improvements in

high frequency ocean color observations: Combining data

from SeaWiFS and MODIS. IEEE Transactions on

Geoscience and Remote Sensing, 36, 1350-1353.

Gregg, Watson W., W.E. Esaias, G.C. Feldman, R. Frouin, S.B.

Hooker, C.R. McClain, and R.H. Woodward., 1998:

Coverage opportunities for global ocean color in a multimission era. IEEE Transactions on Geoscience and Remote

Sensing, 36, 1620-1627.

Woodward, Robert H. and W.W. Gregg 1988: An assessment

of SeaWiFS and MODIS ocean coverage. NASA

Technical Memorandum, 208607, 45 pp

PRESENTATIONS

Bontempi, Paula S., W.W. Gregg, and J.A. Yoder 1998: Initial

observations of springtime surface chlorophyll-a in shelf

waters off the southeastern United States by the Ocean

Color and Temperature Sensor (OCTS). EOS, 79, 28.

Conkright, M.E., W.W. Gregg, and C. Stephens, 1998:

Historical chlorophyll data sets: Intercomparison of the

NODC and NASA. EOS, 79, 91.

Gregg, Watson W., 1998: Initial assessment of ocean color data

from the OCTS mission. EOS, 79, 28.

Gregg, W.W. and M.E. Conkright, 1998: Blended in situ and

remotely-sensed ocean chlorophyll data sets. EOS,

Transactions, American Geophysical Union.

Gregg, W.W. and M.E. Conkright, 1999: Initial results from a

global blended ocean chlorophyll analysis. Proceedings

of the 79th AMS annual meeting, 10th symposium on

global change studies.

Gregg, Watson W., 1999: Considerations for Future Ocean

Color Sensors. in Proceedings of the 8th Japan/US Working

Group on Ocean Color (JUWOC), J. Ishizaka and J.
SIMBIOS Project Annual Report

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Campbell, eds., Earth Science and Technology Organization

98P0A1-D009, Shibaura, Minato-ku.

Gregg, W.W., 1999. Considerations for future ocean color

sensors. Proceedings of the 8th Japan/US Working Group

on Ocean Color (JUWOC), J. Ishizaka and J. Campbell,

eds., Kona, Hawaii, Nov. 1998.
SIMBIOS Project Annual Report

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Chapter 13

Bio-Optical Measurement and Modeling of the California Current

and Polar Oceans

B. Greg Mitchell

Scripps Institution of Oceanography, University of California San Diego, California

13.1 INTRODUCTION

This SIMBIOS project contract supports in situ

ocean optical observations in the California Current,

Southern Ocean, Indian Ocean as well as merger of other

in situ data sets we have collected on various global

cruises supported by separate grants or contracts. The

principal goals of our research are to validate standard or

experimental products through detailed bio-optical and

biogeochemical measurements, and to combine ocean

optical observations with advanced radiative transfer

modeling to contribute to satellite vicarious radiometric

calibration and advanced algorithm development.

In collaboration with major oceanographic shipbased observation programs funded by various agencies

(CalCOFI, US JGOFS, NOAA AMLR, INDOEX and

Japan/East Sea) our SIMBIOS effort has resulted in data

from diverse bio-optical provinces. For these global

deployments we generate a high-quality,

methodologically consistent, data set encompassing a

wide-range of oceanic conditions. Global data collected

in recent years have been integrated with our on-going

CalCOFI database and have been used to evaluate

SeaWiFS algorithms and to carry out validation studies.

The combined database we have assembled now

comprises more than 700 stations and includes

observations for the clearest oligotrophic waters, highly

eutrophic blooms, red-tides and coastal case 2

conditions. The data has been used to validate waterleaving radiance estimated with SeaWiFS as well as biooptical algorithms for chlorophyll pigments. The

comprehensive data is utilized for development of

experimental algorithms (e.g. high-low latitude pigment

transition, phytoplankton absorption, and cDOM).

13.2 RESEARCH ACTIVITIES

A key element of our program includes on-going

deployment on CalCOFI cruises to the California Current

System (CCS) for which we have a 7-year time-series.

This region experiences a large dynamic range of coastal

and open ocean trophic structure and has experienced

strong interannual forcing associated with the El Niño –

La Niña cycle from 1997-2000 (Kahru and Mitchell,

2000; Kahru and Mitchell, in press). CalCOFI data

provides an excellent reference for evaluating our other

global data sets.

During the third year of our contract, we participated

in 3 CalCOFI cruises, one cruise in collaboration with

NOAA AMLR to the Southern Ocean, three cruises in

East Asian marginal seas, and one cruise off the west

coast of Mexico with colleagues from CICESE in

Ensenada, Mexico. The global distribution of our

present data set is shown in Figure 13.1. On all cruises,

an integrated underwater profiling system was used to

collect optical data and to characterize the water column.

The system included an underwater radiometer

(Biospherical Instruments MER-2040 or MER-2048)

measuring depth, downwelling spectral irradiance (Ed)

and upwelling radiance (Lu) in 13 spectral bands. A

MER-2041 deck-mounted reference radiometer

(Biospherical Instruments Inc) provided simultaneous

measurements of above-surface downwelling irradiance.

Details of the profiling procedures, characterization and

calibration of the radiometers, data processing and

quality control are described in Mitchell and Kahru

(1998). The underwater radiometer was also interfaced

with 25 cm transmissometers (SeaTech or WetLabs), a

fluorometer, and SeaBird conductivity and temperature

probes. When available, additional instrumentation

integrated onto the profiling package included AC9

absorption and attenuation meters (WetLabs Inc.), and a

Hydroscat-6 backscattering meter (HobiLabs).

In conjunction with in situ optical measurements,

discrete water samples were collected from a CTDRosette immediately before or after each profile for

additional optical and biogeochemical analyses. Pigment

concentrations were determined fluorometrically and

with HPLC. Spectral absorption coefficients (300-800

nm) of particulate material were estimated by scanning

particles concentrated onto Whatman GF/F filters

(Mitchell 1990) in a dual-beam spectrophotometer
SIMBIOS Project Annual Report

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Figure 13.1. Distribution of in situ optical stations available for algorithm development.

Figure 13.2. Normalized water leaving radiance at SeaWiFS bands plotted against chlorophyll for our global data

set. Blue = CalCOFI, Green = Japan/East Sea; Black = INDOEX; Red = JGOFS Southern Ocean.
SIMBIOS Project Annual Report

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 (Varian Cary 1). Absorption of soluble material was

measured in 10 cm cuvettes after filtering seawater

samples through 0.2 μm pore size polycarbonate filters.

Absorption methods are described in more detail in

Mitchell et al. (2000). We have also been collecting

detailed measurements of other optical and

phytoplankton properties including phycoerythrin

pigment, size distribution using flow cytometry and a

Coulter Multisizer, photosynthesis, and particulate

organic matter (carbon and nitrogen).

13.3 RESEARCH RESULTS

Our normalized water leaving radiances (Lwn) at

SeaWiFS bands for the global data set are plotted against

surface chl-a in Figure 13.2. Our original CalCOFI data

represented approximately 30% of the data used by

O’Reilly et al. (1998) for development of the SeaWiFS

OC2v2 and approximately 25% of the updated OC 4

algorithm (O’Reilly et al., 2000). Previously we used

SeaDAS v3.0 to evaluate OC2v2 estimates of chl-a

compared to a CalCOFI-specific regional algorithm

(CAL-P6) using match-ups collected during CalCOFI

cruises (Kahru and Mitchell, 1999). Here we compare

retrievals for SeaDAS v4.0, which has updated

atmospheric and bio-optical algorithms (Figures 13.3 and

13.4). Whereas OC2v2 in SeaDAS 3.0 tended to

overestimate in situ chl-a at high values and

underestimate at low values (Kahru and Mitchell, 1999),

we find that OC4 in SeaDAS 4.0 tends to underestimate

chl-a at high values, but is very good at low chlorophyll

(Figure 13.3). Even after processing with the new

atmospheric algorithm that eliminates the assumption

that Lwn in the near infrared is zero (Siegel et al., 2000),

SeaDAS v4.0 still has significant underestimates of Lwn

at 412 for all our match-ups (chl-a range 0.1-10.0) with

worse performance at high chl-a (Figure 13.4). Even

Lwn 443 tends to be underestimated over most the range,

but not as severely as Lwn 412. Continued effort is still

required to improve the accuracy of Lwn if we are to be

able to apply multi-wavelength bio-optical retrieval

algorithms that require accurate estimates of Lwn at 412

and 443 (e.g. Garver and Siegel, 1997; Carder et al.,

1999).

For the Southern Ocean data (red crosses in Figure

13.2), there is a strong deviation from our other global

data sets, and much greater variance in the scatter plots.

This region has been shown to have bio-optical

algorithms that are different than low latitude regions

such as CalCOFI (Mitchell and Holm-Hansen, 1991;

Mitchell, 1992). The large variance in the Lwn-chl-a

scatter plots may be attributable in part to very different

community types (e.g. prymnesiophytes, diatoms,

cryptophytes as discussed in Arrigo et al., 1998). Our

results underscore the need for more data to serve as a

basis for regional algorithms to improve estimates of chla from ocean color remote sensing. Regional algorithms

will require procedures to allow transition from low

latitude to high latitude without introducing errors at the

lower latitudes. More data and advanced models are

required to resolve issues regarding Southern Ocean biooptical algorithms, and the causes of observed

differentiation within the region as well as differences

between the Southern Ocean and lower latitudes. For a

better understanding, it is essential to determine not only

reflectance and chlorophyll, but also inherent optical

properties including absorption and backscattering as

reported by Reynolds et al. (In press). Generally, there

are few observations in the Southern Ocean, and even

fewer with detailed observations including inherent

optical properties. We lack combined pigment and

optical observations in the extremely low chlorophyll

regions that can be observed in the SeaWiFS images for

the southern Pacific Ocean sector west of the Drake

Passage, and the southern Indian Ocean sector west of

Kerguelan Island. These two regions represent very low

satellite-derived chlorophyll, which never exceed values

of 0.2 mg chl-a m-3.

A significant issue that has arisen within the

SIMBIOS community is the fidelity of chl-a estimates

using either HPLC or fluorometric methods. We have

completed analysis and quality control for more than 800

samples taken from the same water sampling bottles

during CalCOFI cruises (Figure 13.5). The fluorometric

method is described in Venrick, et al., (1984) and the

HPLC method is described in Goericke and Repeta

(1993). We find that there is excellent overall agreement

with a nearly 1:1 relationship, however individual

samples routinely differ by up to30-40%. For the

JGOFS Southern Ocean Polar Front cruises, the

discrepancies were much larger and are still unresolved.

A high priority for SIMBIOS should be to ensure the

highest possible quality of pigment estimates, which will

require consistent implementation of rigorous protocols.

As a contribution to this effort, we participated in the

SIMBIOS pigment round robin experiment during the

past year.

15.4 CONCLUSIONS

With renewal of our SIMBIOS contract, we will

continue our approach of acquiring detailed, high quality

data sets at the global scale. We will continue to

participate in CalCOFI cruises. In 2001 we will also
SIMBIOS Project Annual Report

94

participate in the NOAA AMLR cruise to the Southern

Ocean and the NOAA ACE-Asia cruise to the western

sub-tropical Pacific, East China Sea, and Yellow Sea. A

detailed set of spectral reflectance, absorption,

backscattering, pigment, and particle size structure will

be determined on most cruises. A new free-fall

radiometer will be acquired with 19 channels (310-700

nm) for determining both downwelling and upwelling

irradiance, and upwelling radiance for all spectral bands.

We have shown that measuring these three radiometric

geometries with our MER 2048 allowed us to retrieve

backscatter and absorption coefficients (Stramska et al.,

2000). We will continue our modeling efforts to

improve our understanding of regional bio-optical

properties and their relationship to biogeochemical

parameters (e.g. Reynolds et al, in press; Loisel et al.,

submitted). Our goal is to develop appropriate regional

parameterizations for semi-analytical inversion models

for the retrieval of inherent optical properties as well as

biogeochemical properties besides chl-a in addition to

continuing our validation work for standard ocean color

satellite algorithms.

REFERENCES

Arrigo, K.R., D.H. Robinson, D.L. Worthen, B. Schieber

and M.P. Lizotte, 1998: Bio-optical properties of the

southwestern Ross Sea. Journal of Geophysical

Research. 103(C10), 21,683-21,695

Carder, K.L., F.R. Chen, Z.P. Lee and S.K. Hawes, 1999:

Semianalytic moderate-resolution imaging

spectrometer algorithms for chlorophyll a and

absorption with bio-optical domains based on

nitrate-depletion temperatures. Journal of Geophysical Research. 104(C3), 5,403-5,421

Garver, S.A. and D.A. Siegel, 1997: Inherent Optical

Property Inversion of Ocean Color Spectra and its

Biogeochemical Interpretation: 1. Time series from

the Sargasso Sea. Journal of Geophysical Research.

102(C8), 18,607-18,625

Goericke, R. and D.J. Repeta, 1993: Chlorophylls a and

b and divinyl chlorophylls a and b in the open

subtropical North Atlantic Ocean. Marine Ecology

Progress Series. 101, 307-313

Kahru, M. and B.G. Mitchell, 1999: Empirical

chlorophyll algorithm and preliminary SeaWiFS

validation for the California Current. International

Journal of Remote Sensing. 20(17) , 3,423-3,430

Kahru, M. and B.G. Mitchell, 2000: Influence of the

1997-98 El Niño on the surface chlorophyll in the

California Current. Geophysical Research Letters.

27(18), 2,937-2,940

Kahru, M. and B.G. Mitchell, 2000: Seasonal and nonseasonal variability of satellite-derived chlorophyll

and CDOM concentration in the California Current.

Journal of Geophysical Research. (in press)

Loisel, H. and D. Stramski, 2000: Estimation of the

inherent optical properties of natural waters from the

irradiance attenuation coefficient and reflectance in

the presence of Raman scattering. Applied Optics.

39(18), 3,001-3,011

Mitchell, B.G., 1990: Algorithms for determining the

absorption coefficient of aquatic particulates using

the quantitative filter technique (QFT). In: Spinrad,

R., ed., Ocean Optics X, Bellingham, Washington,

SPIE, pp. 137-148

Mitchell, B.G. (1992) Predictive bio-optical relationships

for polar oceans and marginal ice zones. Journal of

Marine Systems,. 3 ,91-105

Mitchell, B.G. and M. Kahru, 1998: Algorithms for

SeaWiFS standard products developed with the

CalCOFI bio-optical data set.

Cal.Coop.Ocean.Fish.Invest.R,. 39, 133-147

Mitchell, B.G. and O. Holm-Hansen, 1991: Bio-optical

properties of Antarctic Peninsula waters:

Differentiation from temperate ocean models. DeepSea Research I. 38(8/9), 1,009-1,028

Mitchell, B.G., A. Bricaud, K.L. Carder, J.S. Cleveland,

G.M. Ferrari, R. Gould, M. Kahru, et al., 2000:

Determination of spectral absorption coefficients of

particles, dissolved material and phytoplankton for

discrete water samples. In: NASA, Ocean Optics

Protocols for Satellite Ocean Color Sensor

Validation.

O'Reilly, J.E., S. Maritorena, B.G. Mitchell, D.A. Siegel,

K.L. Carder, S.A. Garver, M. Kahru and C.

McClain, 1998: Ocean color chlorophyll algorithms

for SeaWiFS. Journal of Geophysical Research.

103(C11) , 24,937-24,953

O'Reilly, J.E., S. Maritorena, D.A. Siegel, M.C. O'Brien,

D.A. Toole, B.G. Mitchell, M. Kahru, et al., 2000:

Ocean color chlorophyll a algorithms for SeaWiFS,

OC2 and OC4: Version 4. In: NASA, SeaWiFS

Postlaunch Calibration and Validation Analyses.
SIMBIOS Project Annual Report

95

Figure 13.3. Chlorophyll-a estimates derived using SeaWiFS SeaDAS version 4.0 compared to in situ estimates of

chlorophyll-a for NASA's global processing version 3.0 OC4 algorithm, and our CAL-P6 algorithm.

Figure 13.4. Normalized water leaving radiance derived from SeaWiFS SeaDAS version 4.0 processing compared to

in situ measurements at the 6 visible bands of SeaWiFS.
SIMBIOS Project Annual Report

96

Figure 13.5. CalCOFI HPLC total chlorophyll-a (mono-vinyl and di-vinyl chl-a, and closely related derivates) plotted against

fluorometric chlorophyll-a. The 1:1 and regression fits are indicated by the solid and dashed lines, respectively.

Reynolds, R.A., D. Stramski and B.G. Mitchell, 2000: A

chlorophyll-dependent semi analytical reflectance

model from derived from field measurements of

absorption and back scattering coefficients within

the Southern Ocean. Journal of Geophysical

Research. (In Press)

Siegel, D.A., M. Wang, S. Maritorena and W. Robinson,

2000: Atmospheric correction of satellite ocean

color imagery: The black pixel assumption.

Appl.Opt. 39(21) , 3,582-3,591

Stramska, M., D. Stramski, B.G. Mitchell and C.D.

Mobley, 2000: Estimation of the absorption and

backscattering coefficients from in-water

radiometric measurements. Limnology and

Oceanography. 45(3), 628-641

Venrick, E.L. and T.L. Hayward, 1984: Determining

chlorophyll on the 1984 CalCOFI surveys. CalCOFI

Reports., XXV, 74-79
SIMBIOS Project Annual Report

97

This research was supported by the

SIMBIOS NASA contract # 97130

PEER REVIEWED PUBLICATIONS

Gross, L., S. Thiria, R. Frouin, and B.G. Mitchell, 2000

Artificial neural networks for modeling the transfer

function between marine reflectance and phytoplankton

pigment concentration. Journal of Geophysical Research.

105(C2), 3,483-3,495.

Kahru, M. and B.G. Mitchell, 1999: Empirical chlorophyll

algorithm and preliminary SeaWiFS validation for the

California Current, International Journal of Remote

Sensing 20(17), 3,423-3,429.

Kahru, M., and B.G. Mitchell, 2000. Seasonal and nonseasonal variability of satellite-derived chlorophyll and

CDOM concentrations in the California Current. Journal

of Geophysical Research. (In Press)

Mitchell, B.G. and M. Kahru, 1998: Algorithms for SeaWiFS

standard products developed with the CalCOFI biooptical data set. CalCOFI Reports, Vol. 39: 133-147.

Nakajima, T., A. Higurashi, K. Aoki, T. Endoh, H.

Fukushima, M. Toratani, Y. Mitomi, B.G. Mitchell and R.

Frouin, 1999: Early phase analysis of OCTS radiance data

for aerosol remote sensing, IEEE Transactions, 37(3),

1,575-1,585.

O'Reilly, J.E., S. Maritorena, B.G. Mitchell, D.A. Siegel, K.L.

Carder, S.A. Garver, M. Kahru, C. McClain, 1998: Ocean

Color Chlorophyll Algorithms for SeaWiFS, Journal of

Geophysical Research, 103(C11), 24,937-24,953.

Reynolds, R.A., S. Stramski and B.G. Mitchell, 2000: A

chlorophyll-dependent semianalytical reflectance model

derived from field measurements of absorption and

backscattering coefficient within the Southern Ocean.

Journal of Geophysical Research. (In Press)

Stramska, M., D. Stramski, B.G. Mitchell and C.Mobley,

2000: Estimation of the absorption and backscattering

coefficients from in-water radiometric measurements,

Limnology and Oceanography, 45(3), 628-641

Submitted

Loisel, H., D. Stramski, B.G. Mitchell, F. Fell, V. Fournier, B.

Lemasle and M. Babin , 2000: Comparison of the ocean

inherent optical properties obtained from measurements

and inverse modeling. Submitted to Applied Optics.

Reynolds, R.A., S. Stramski and B.G. Mitchell, 1999: A

chlorophyll-dependent semianalytical reflectance model

derived from field measurements of absorption and

backscattering coefficient within the Southern Ocean.

Submitted to Journal of Geophysical Research.

Stramski, M., D. Stramski, B.G. Mitchell and C.Mobley,

1999: Estimation of the absorption and backscattering

coefficients from in-water radiometric measurements,

Submitted to Limnology and Oceanography.

PRESENTATIONS

Flatau, P.J., B. Bichnevicius, B.G. Mitchell, 1998:Vicarious

calibration of the absorption and scattering measurements

in the California Current, AGU Ocean Science Meeting,

San Diego.

Fougnie, B., P.-Y. Deschamps, R. Frouin, and B.G. Mitchell,

1998: Measuring Water-Leaving Radiance with a

Polarization Radiometer: Theory and Experimental

Verification, AGU Conference, San Diego.

Frouin, R., P.-Y. Deschamps, B.G. Mitchell and M. Kahru,

1998: The Normalized Difference Phytoplankton Index

for Satellite Ocean Color Applications, AGU Ocean

Science Meeting, San Diego.

Kahru, M. and B.G. Mitchell, 1998: Empirical chlorophyll

algorithm for the California Current, AGU,San Francisco.

Kahru, M. and B.G. Mitchell, 2000, Seasonal and nonseasonal variability of satellite-derived chlorophyll and

CDOM concentration in the California Current. AGU

Ocean Sciences Meeting, San Antonio, TX

Loisel, H., D. Stramski, M. Babin, F. Fell and B.G. Mitchell,

2000, An inverse model for the estimation of the inherent

optical properties in presence of Raman scattering:
SIMBIOS Project Annual Report

98

description and validation. AGU Ocean Sciences

Meeting, San Antonio, TX

Mitchell, B.G., 2000, Global bio-optical algorithms and ocean

color satellite validation for ocean color. August 21 -

September 9, 2000. Satellite Oceanography 2000, Focus

at the sea surface, Department of Physical Oceanography,

CICESE, Ensenada B.C., MEXICO

Mitchell, B.G., M. Kahru, R. Reynolds, J. Wieland, D.

Stramski 2000 Satellite Estimation Of Seasonal

Variations In Organic Carbon To Chlorophyll-A Ratios In

The Southern Ocean And Interpretation Of Carbon Flux

Dynamics. AGU Ocean Sciences Meeting, San Antonio,

TX

Mitchell, B.G., M. Kahru, R.A. Reynolds, J.D. Wieland and

D. Stramski, 2000, Satellite Estimation Of Seasonal

Variations In Organic Carbon To Chlorophyll-A Ratios In

The Southern Ocean Oceans from Space Symposium.

October 9 - 13, 2000, Venice, Italy

Reynolds, R.A., M. Kahru, J.D. Wieland, D. Stramski and

B.G. Mitchell, 1998: An evaluation of SeaWiFS ocean

color chlorophyll algorithms within the Southern Ocean,

AGU, San Francisco.

Stramski, D., R.A. Reynolds, M. Kahru, J.D. Wieland and

B.G. Mitchell 2000 Geographic distribution and seasonal

variations in particulate organic carbon within the

Southern Ocean as determined from satellite imagery of

ocean color. AGU Ocean Sciences Meeting, San

Antonio, TX
SIMBIOS Project Annual Report

99

Chapter 14

SIMBIOS Normalized Water-Leaving Radiance Calibration and

Validation: Sensor Response, Atmospheric Corrections, Stray

Light and Sun Glint

James L. Mueller

Center for Hydro-Optics and Remote Sensing, San Diego State University, California

14.1 INTRODUCTION

This SIMBIOS contract supports acquisition of match up

radiometric and bio-optical data for validation of SeaWiFS

and other ocean color satellites, and evaluation of uncertainty

budgets and protocols for in situ measurements of normalized

water leaving radiances.

14.2 RESEARCH ACTIVITIES

Research activities during the third and final year of this

contract break down into three areas:

1. Data Acquisition and Analysis: we supported, but did not

participate in, one cruise in the Gulf of California

(GOCAL 99B) under this contract. This work included

pre-cruise radiometric calibrations, near real-time

interpretative analysis of SeaWiFS imagery and

communication of mesoscale front and eddy locations to

the ship, and post-cruise data analysis for SeaBASS

archival. GoCAL 99B was the final

CHORS/CICESE/OSU collaborative cruise associated

with the SIMBIOS project.

2. Ocean Optics Protocols for normalized water-leaving

radiance measurements and analyses methods, and

uncertainty budgets, were reviewed, evaluated and

updated.

3. Modeling: A set of radiative transfer model experiments

were undertaken to evaluate selected aspects of

normalized remote-sensing reflectance uncertainties.

14.3 RESEARCH RESULTS

PRR radiometric profiles and phytoplankton pigment

concentration data acquired during GoCAL 99B were

analyzed and formatted for archival in SeaBASS. Analyses

and archival preparation of data from GoCAL 98B and 99A

were also completed during the period. Our manuscript

describing bio-optical provinces of the Gulf of California, and

a remote sensing algorithm for estimating euphotic zone

depth, was revised and published (Cervantes-Duarte et al.

2000).

A comprehensive evaluation and update of progress in

protocols for in situ determinations of Normalized WaterLeaving Radiance (or equivalently, Normalized RemoteSensing Reflectance) was completed and published (Mueller

2000a, 2000b; Mueller et al. 2000). These topics included

determinations of LW and RRS from both in-water (Mueller

2000b) and above-water (Mueller et al. 2000) radiometric

measurements, and the normalization procedure to determine

LWN and RRSN from the measured quantities, solar zenith angle

and inherent optical properties parameterized by the method of

Morel and Gentili (1996) (Mueller 2000a).

A supplemental radiative transfer modeling task was

undertaken to estimate the uncertainty associated with the

Morel and Gentili (1996) remote-sensing [Chl]

parameterization of the inherent optical properties. This has

been approached by comparing measured RRSN (from in-water

PRR profiles) with that calculated using measured IOP in the

commercially available Hydrolight radiative transfer model

for station data from the GoCAL cruises.

At the request of the SIMBIOS Project Office (SPO), the

Principal Investigator accepted a high-priority task to

completely and comprehensively revise the ocean optics

protocols (Mueller and Austin 1995) to reflect the

considerable progress made in many areas over the previous 5

years. The revised protocol document was completed and

published in August (Fargion and Mueller 2000). The overall

scope of this revision greatly exceeded the 3 LWN chapters

cited above. The additional work was supported by the SPO

under a separate contract. The priority given to the overall

protocol revision task necessarily delayed progress in the other

research efforts under this contract, and accordingly, delivery

schedule waivers were requested and approved by the SPO.

14.4 CONCLUSIONS

The research and results outline above complete work on

the present SIMBIOS contract. The Principal Investigator’s
SIMBIOS Project Annual Report

100

future research efforts within the SIMBIOS program, under

terms of a new contract with the SPO, will be devoted entirely

to preparation and publication of annual revisions to Ocean

Optics Protocols (Fargion and Mueller 2000). Each revision

will reflect annual progress and improvements in ocean optical

and bio-optical instrumentation, measurement methods and

methods of data analysis. The protocol revisions will be

drawn from a combination of peer-reviewed literature,

workshop results, and collaborative written reviews reflecting

realizations of community consensus on recent advances in

these methods. Revision 3 to the protocols will be prepared

and delivered to the SPO on 1 October 2001, for

administrative review and publication by 1 December 2001.

To allow this schedule, draft protocol materials received later

than July 2001 will be automatically deferred and considered

for publication in a later revision. It is anticipated that

Revisions 4 and 5 will be prepared and delivered on a similar

schedule in 2002 and 2003, respectively.

REFERENCES

Fargion G.S. and J.L. Mueller, 2000. Ocean Optics Protocols

for Satellite Ocean Color Sensor Validation, Revision 2.

NASA/TM-2000-209966. NASA Goddard Space Flight

Center, Greenbelt, MD. 184pp.

Morel, A. and B. Gentili, 1996. Diffuse reflectance of oceanic

waters. III. Implication of bidirectionality for the remotesensing problem. Appl. Opt., 35(24), 4850-4862.

Mueller, J.L., 2000a. Overview of measurement and data

analysis protocols. In: Fargion, G.S. and J.L Mueller,

2000. Ocean Optics Protocols for Satellite Ocean Color

Sensor Validation, Revision 2. NASA/TM-2000-209966.

NASA Goddard Space Flight Center, Greenbelt, MD.

(Chapter 8) pp65 – 86.

Mueller. J.L., 2000b. In-water radiometric profile

measurements and data analysis protocols. In: Fargion,

G.S. and J.L Mueller, 2000. Ocean Optics Protocols for

Satellite Ocean Color Sensor Validation, Revision 2.

NASA/TM-2000-209966. NASA Goddard Space Flight

Center, Greenbelt, MD. (Chapter 9) pp87-97.

Mueller, J.L., et al., 2000. Above-water radiance and remote

sensing reflectance measurements and data analysis

protocols. In: Fargion, G.S. and J.L Mueller, 2000.

Ocean Optics Protocols for Satellite Ocean Color Sensor

Validation, Revision 2. NASA/TM-2000-209966.

NASA Goddard Space Flight Center, Greenbelt, MD.

(Chapter 10) pp98-107.

This research was supported by the

SIMBIOS NASA contract # 97126

PEER REVIEWED PUBLICATIONS

Barnard, A.H., J.R.V. Zaneveld, W.S. Pegau, J.L Mueller, H.

Maske, R. Lara-Lara, S. Alvarez-Borrego, and E. ValdezHolquin. 1999: The determination of PAR levels from

absorption coefficient profiles at 490 nm. Ciencias

Marinas, in press.

Cervantes-Duarte, R., J. L. Mueller, C.C. Trees, H. Maske, S.

Alvarez-Borrego and R. Lara-Lara, 2000. Euphotic

depth, irradiance attenuation and remote sensing K490 in

bio-optical provinces of the Gulf of California. Ciencias

Marinas, 26(4) ,533-560.

Mueller, J.L., J.R.V. Zaneveld, W.S. Pegau, E. Valdez, H.

Maske, S. Alvarrez-Borrego, and R. Lara-Lara. 1997:

Remote sensing reflectance: preliminary comparisons

between in-water and above-water measurements, and

estimates modelled from measured inherent optical

properties. SPIE, 2963, 502-513.

Zaneveld, J.R.V., W.S. Pegau, A.H. Barnard, J.L. Mueller, H.

Maske, E. Valdez, R. Lara-Lara, and S. Alvarez-Borreo.

1997: Prediction of euphotic depths and diffuse

attenuation coefficients from absorption profiles: A

model based on comparison between vertical profiles of

spectral absorption, spectral irradiance, and PAR. SPIE,

2963, 585-590

PRESENTATIONS

Pegau, W.S., A.H. Barnard, J.R.V. Zaneveld, J.L. Mueller, H.

Maske, R. Lara-Lara, S. Alvarez-Borrego, E. ValdezHolguin, and R. Cervantes-Duarte, 1998: Optical

variability in the Gulf of California. Ocean Optics XIV

Conference Papers, Volume 2, Kailua-Kona, HI.
SIMBIOS Project Annual Report

101

Chapter 15

Validation of Carbon Flux & Related Products for SIMBIOS: the

CARIACO Continental Margin Time Series & the Orinoco River

Plume

Frank Muller-Karger,Chuanmin Hu and John P. Akl

University of South Florida, Department of Marine Science, St. Petersburg, Florida

Ramon Varela

Fundacion La Salle de Ciencias Naturales, Punta de Piedras, Edo. Nueva Esparta, Venezuela

15.1 INTRODUCTION

Between 1997 and 2000, this SIMBIOS investigation

collected bio-optical measurements in the Southeastern

Caribbean Sea and the tropical western Atlantic to help

understand the color of coastal and continental shelf waters.

Specifically, bio-optical data were collected to complement

an oceanographic time series maintained within the Cariaco

Basin, a site affected by seasonal coastal upwelling. Biooptical data were also collected within the plume of the

Orinoco River during seasonal extremes in discharge. This

program focused on providing data to the SeaWiFS and

SIMBIOS Projects for validating SeaWiFS products. The

data are unique in that they provide a substantial number of

observations on repeated seasonal cycles for the SeaBASS

bio-optical database.

An important aspect of this SIMBIOS investigation was

a focus on proper interpretation of ocean color remote

sensing data from coastal and continental shelf

environments. With this goal in mind, ocean color satellite

data from a variety and locations and from different satellite

sensors were examined to understand spatial and temporal

variability in pigment concentrations, and also to conduct an

in-depth study of current atmospheric correction and biooptical algorithms.

15.2 RESEARCH ACTIVITIES

Here we provide an update on activities during 1999-

2000 and a summary of contributions made to SIMBIOS

over the past three years. Background information on this

SIMBIOS contract is provided in Muller-Karger (1999) and

Muller-Karger et al. (1999).

The CARIACO Time Series

This SIMBIOS effort conducted monthly bio-optical

observations at 10.5°N, 64.67°W within the Cariaco Basin

(Figure 15.1) in the southeastern Caribbean Sea. This was

part of a multidisciplinary program referred to as CARIACO

(CArbon Retention In A Colored Ocean) that received

support from the U.S. National Science Foundation and the

Consejo Nacional de Investigaciones Cientificas y

Tecnologicas (CONICIT) of Venezuela for oceanographic

cruises and for investigations focusing on processes

affecting particulate carbon flux to the bottom of the Cariaco

Basin. Initial results from the CARIACO program are

presented by Muller-Karger et al. (2000a, 2000b) and

Thunell et al. (2000).

During each cruise, we collected a series of bio-optical

measurements. In 1997 and 1998, we used the underwater

profiling Biospherical Instruments MER2048 in conjunction

with a MER2041 Deck Cell. Since mid-1998, we have used

a PRR-600 profiler with matching deck cell. Above water

measurements were made with a Photo Research

Hyperspectral Colorimeter Model PR650. Derived products

include Lw (Water-Leaving Radiance), Rrs (Remote-sensed

reflectance) and K (attenuation coefficient). In addition, a

full suite of measurements is made which includes:

particulate material concentration and absorption

coefficients, HPLC, fluorometric determinations of Chl

concentration, pH, Alkalinity, primary productivity, DOC

absorption and concentration, nutrients, sun photometry,

oxygen, and salinity.

As of October 2000, data from 30 CARIACO cruises

had been submitted to the SeaBASS archive. Figure 15.2

presents results of the time series, comparing in situ

observations with chlorophyll-a estimates based on abovewater reflectance measurements and using the bio-optical

algorithms adopted by the SeaWiFS project (O'Reilly et al.,

1998; O’Reilly et al., 2000).
SIMBIOS Project Annual Report

102

Figure 15.1. The Cariaco Basin: bathymetry and the location

of the CARIACO time series station.

The Orinoco River Plume

Muller-Karger et al. (1989) and Muller-Karger and

Varela (1990) describe a seasonal cycle in the spatial

structure of near-surface pigment detected with Coastal Zone

Color Scanner (CZCS) data collected over the eastern

Caribbean Sea. In addition to describing the seasonal

occurrence of large pigment patches associated with coastal

upwelling, as seen in the Cariaco Basin, they discovered that

the seasonally-expanding plume of the Orinoco River

reached Puerto Rico around September-October and drifted

westward, slowly losing its color signature. The Orinoco

dispersed pigment over > 3x105 km2 of the Caribbean Sea.

Clearly, while there are phytoplankton within the plume, the

plume represents Case II waters in which colored dissolved

organic matter (Yellow Substance) and suspended sediment

lead to erroneous satellite-derived pigment products. The

Orinoco River provides a good study region for extreme

Case II waters which can easily be covered by satellite

observations at a variety of spatial and temporal resolutions.

We carried out five cruises to the Orinoco Delta and

plume under this SIMBIOS program. Stations were occupied

north of Dragon's Mouth (the northern strait between

Venezuela and Trinidad), within the Gulf of Paria,

immediately off the delta of the Orinoco River south of

Serpent's Mouth (the southern strait between Venezuela and

Trinidad), and within several branches of the Orinoco Delta.

Figure 4 shows station locations for the first four Orinoco

River cruises. As of October 2000, data from five Orinoco

River cruises had been submitted to the SeaBASS archive.

An additional Orinoco cruise is scheduled for late October

2000.

Above-water spectral remote-sensing reflectance was

measured including corrections for sky-radiance initially

using a Photo Research Hyperspectral Colorimeter Model

PR650, and starting in 1999 an Analytical Spectral Devices

(ASD) spectrometer which extended the spectral range of

observations, namely from 380 to 1,200 nm. We also

conducted underwater measurements of remote sensing

reflectance using submersible MER2048 and, since 1998,

PRR-600 instrumentation (Biospherical Instr.). Some

observations were conducted away from the coast, where the

color of the plume is influenced by colored dissolved

organic matter as well as phytoplankton, but where

suspended terrestrial material is expected to be minimal. We

also collected data very close to the coast where the plume is

extremely turbid.

Figure 15.5 shows the variability obtained in the

absorption coefficient for colored dissolved organic matter

relative to variation in salinity during the first four cruises.

These data will help us to understand the dependence of

variability in absorption to geographical location and season.

The river plume data are being used by Joe Salisbury

and Charles Vorosmarty at the University of New

Hampshire for automated modeling of river water impact off

the continent of South America, in a model linked to

terrestrial hydrology.

SeaWiFS Data Calibration Efforts

Dr. Chuanmin Hu from our group forwarded a

Gain/CalibrationTable study summary to Chuck McClain,

which we believe was used in evaluating the new SeaWiFS

calibration. Specifically, during January – May 1998 we

found that the normalized water leaving radiance, nLw555,

for clear water was frequently lower than 0.25 (not to

mention the well-accepted value, 0.28, from Gordon and

Clark). After studying several images very carefully, we

found that the calibration table released at that time was

probably inappropriate. We forwarded detailed analyses to

the SeaWiFS Project outlining our results.

Algorithm development

Dr. Hu (Hu et al., 2000a, b) developed and implemented

a turbid-water (case II) atmospheric correction procedure for

ocean color satellite sensors. It uses a nearest-neighbor

method to propagate the aerosol type derived over adjacent

clear water to apply over turbid water. The advantage of this

procedure over an iterative approach is that the dependence

on the under-water optics is minimized, therefore it can be

applied to unknown type of water or shallow water where

bottom reflection may contribute to the sensor signal. The

procedure is particularly useful for turbid coastal or shallow

inland waters

Errors in SeaWiFS products caused by cirrus clouds,

high altitude aerosols, or digitization-noise were analyzed by

Hu et al. (2000c). The use of an alternative band in the

atmospheric correction to avoid the O2 absorption lines can

greatly reduce the errors. It was also found that the SeaWiFS

mission goals, namely to estimate water-leaving radiance to

within 5% uncertainly and chlorophyll to within 35% can’t

always be achieved merely because of digitization round-off
SIMBIOS Project Annual Report

103

Figure 15.2 Temporal variation in chlorophyll-a at the CARIACO station (February 1997-June 1999). The values for

fluorometry and HPLC were obtained at approximately 1 m depth while the values for the OC algorithms were

calculated from above-water reflectance measurements using O’Reilly et al. (2000)

Figure 15.3 Temporal variation in the difference (%) between SeaWiFS-derived chlorophyll-a and in situ observations

of chlorophyll-a at the CARIACO station (February 1997-June 1999).

OC4v4vscla-od.xls

400 600 800 1000 1200 1400

Time (days)

-100

0

100

200

300

400

Percentage difference

SeaWiFS/Flourometric data

SeaWiFS/HPLC data

Percentage difference of [Chl a] derived from reflectance (OC4v4) with respect to in situ [Chl a] averaged over

one optical depth for the CARIACO Station.

OC4v4

OC2v2

Temporal variation in [Chl a] at the CARIACO Station (Feb. 1997 - Jun. 1999). The values for fluorometry

and HPLC were obtained at approximately 1 m depth while the values for the algorithms OC2v2, OC2v4,

and OC4v4 were calculated from reflectance measurements above the surface.

Fluorometry

HPLC

OC2v4

1997 1998 1999 Cla_real_od.PDW

400 500 600 700 800 900 1000 1100 1200 1300 1400

Time (days)

0.1

1

10

[Chl a] (mg m-3)
SIMBIOS Project Annual Report

104

Figure 15.4 Cruise tracks for Orinoco Plume cruises 1-4, conducted 23-29 June, 1998 (S_1), 27-30 October,

1998 (S_2), 23-27 February, 1999 (S_3), and 26-31 October, 1999 (S_4).

and systematic noise. Use of an alternative band in the

atmospheric correction or performing a 3x3 smoothing for

the atmospheric-correction bands can greatly reduce this

type of errors. The results are useful for data validation

protocols.

We also developed and implemented a multi-platform

approach for atmospheric correction of Landsat-like sensors

using SeaWiFS/MODIS derived atmospheric parameters

(Hu et al, 2000d). The procedure is useful for combining the

high accuracy of atmospheric correction of ocean-color

satellite and high spatial resolution of Landsat-like sensors

to study coastal processes.

SeaWiFS data processing improvements

Our group implemented an extensive SeaWiFS batch

processing system for use with IDLand SeaDAS. We also

implemented a series of convenient SeaWiFS data analysis

tools based on IDL and IDL On the Net (ION), to allow

analyses over the world wide web.

We found several problems with SeaDAS and interacted

with the SeaDAS Development Group to solve them.

Specifically, we found bugs in the bL2map and histogram

routines, as well as in the more recent implementation of

selection menus for flag products, and pin-pointed a problem

that led to a 'Windowing effect' in the level 2 processing of

SeaWiFS data. Solutions were found for these problems.
SIMBIOS Project Annual Report

105

Figure 15.5. Relationship between the absorption coefficient for colored dissolved organic matter and salinity within

the Orinoco Plume.

We also addressed the speckling problem found in

processed SeaWiFS data. We suggested to Chuck McClain

that a fine tune of the epsilon retrieval software may be

necessary due to the high correlation between epsilon and

chlorophyll.

Additional Field Campaigns and Data Validation

Participated in the Northeast Gulf of Mexico (NEGOM)

program sponsored by the US Mineral Management Services.

Eight 2-week cruises have been conducted in continental

margin waters to the 1000-m isobath off Florida and Alabama.

Extensive data sets were collected, among which included

along-track flow-through chlorophyll and CDOM

fluorescence, salinity and temperature, and sample collection

and optical measurement at discrete stations (~50 for each

cruise). Data analysis is still underway, while some

preliminary results have been presented in a number of

conferences and submitted to the International Journal of

Remote Sensing.

Data Synthesis

We have generated an extensive list of publications based

on our SIMBIOS and related research (see section labeled

“Peer-Reviewed Publications“ below. There will be additional

publications based on the data collected via this SIMBIOS

activity. The CARIACO station and Orinoco Plume data are

central to the research of four graduate students, namely two

Ph. D. and two Masters students.
SIMBIOS Project Annual Report

106

ACKNOWLEDGEMENTS

This work was supported by the National Science

Foundation (NSF Grant OCE 9216626), the National

Aeronautics and Space Administration (NASA Grants NAG5-

6448 and NAS5-97128), and the Consejo Nacional de

Investigaciones Cientificas y Tecnologicas (CONICIT,

Venezuela). Many other people have contributed substantially

to the success of this program. We are indebted to the

personnel of the Fundacion La Salle de Ciencias

Naturales/Estacion de Investigaciones Marinas Isla Margarita

(FLASA/EDIMAR) for their enthusiasm and professional

support. In particular we thank FLASA's Director, Dr.

Hermano Gines, for his confidence in our activities and the

crew of the R/V Hermano Gines (FLASA) for their able

support at sea. Field bio-optical measurements were conducted

primarily by Natasha Rondon and John Akl of FLASA. They,

and Yrene Astor and Ana Lucia Odriozola have processed the

majority of the data to satisfy the seabass report formats.

Jonathan Garcia (also at FLASA) has processed the samples

for particulate and dissolved absorption coefficients. Juan

Capelo and Javier Gutierrez (FLASA) have assisted with the

primary production observations. Robert Thunell and Eric

Tappa (U. South Carolina) maintain the sediment trapping

program and process our particulate samples (POC, PON).

Mary Scranton and Gordon Taylor (SUNY) conduct bacteria

productivity studies. Dissolved Organic Carbon samples were

processed by Ed Peltzer (formerly at the Woods Hole

Oceanographic Institution and currently at the Monterrey Bay

Aquarium Research Institute), and lately by David J.

Hirschberg of State University of New York at Stony Brook.

REFERENCES

Hu, C., K. L. Carder, and F. E. Muller-Karger. 2000a:

Atmospheric correction of SeaWIFS imagery over turbid

coastal waters; a practical method. Remote Sensing of

Environment 74(2), 195-206.

Hu, C., K. L. Carder, and F. E. Muller-Karger. 2000b:

Atmospheric correction of SeaWiFS imagery: assessment

of the use of alternative bands. Applied Optics, 39(21),

3573-3581.

Hu, C., K. L. Carder, F. E. Muller-Karger. 2000c: How precise

are SeaWiFS ocean color estimates? Implications from

digitization-noise errors. Remote Sensing of Environment

(submitted)

Hu, C., K. L. Carder, and F. Muller-Karger. 2000d: A multiplatform approach to atmospheric correctionand

calibration of LANDSAT-7/ETM= imagery over aquatic

environments. Remote Sensing of Environment.,

(Submitted)

Muller-Karger, F. E., C. R. McClain, T. R. Fisher, W. E.

Esaias, and R. Varela. 1989: Pigment distribution in the

Caribbean Sea: Observations from Space. Progress in

Oceanography, 23, 23-69.

Muller-Karger, F. E., and R. Varela. 1990: Influjo del Rio

Orinoco en el Mar Caribe: observaciones con el CZCS

desde el espacio. Memoria. Sociedad de Ciencias

Naturales La Salle. Caracas, Venezuela. Tomo IL,

numero 131-132; Tomo L, numero 133-134, 361-390.

Muller-Karger, F. E. 1999: Validation of Carbon Flux and

Related Products for SIMBIOS: The CARIACO

Continental Margin Time Series. SIMBIOS Project 1998

Annual Report. Edited by: C. R. McCLain and G. S.

Fargion. NASA TM 1999-208645. Chapter 19. Pages 71-

73.

Muller-Karger, F. E., C. Hu., and R. Varela. 1999: Validation

of Carbon Flux and Related Products for SIMBIOS: The

CARIACO Continental Margin Time Series and the

Orinoco River Plume. SIMBIOS Project 1999 Annual

Report. Edited by: C. R. McCLain and G. S. Fargion.

NASA TM 1999-209486. Chapter 17. Pages 88-91.

Muller-Karger, F. E. R. Varela, R. Thunell, M. Scranton, R.

Bohrer, G. Taylor, J. Capelo, Y. Astor, E. Tappa, T. Y.

Ho, and J. J. Walsh. 2000a: The Annual Cycle of Primary

Production in the Cariaco Basin: Implications for Vertical

Export of Carbon Along a Continental Margin. Journal of

Geophysical Research, (In press).

Muller-Karger, F., R. Varela, R. Thunell, M. Scranton, R.

Bohrer, G. Taylor, J. Capelo, Y. Astor, E. Tappa, T.-Y.

Ho, M. Iabichella, J. J. Walsh, and J. R. Diaz. 2000b: The

CARIACO Project: Understanding the Link between the

Ocean Surface and the Sinking Flux of Particulate Carbon

in the Cariaco BasinEOS. AGU Transactions, (In press).

O'Reilly, J. E., S. Maritorena, B. G. Mitchell, D. A. Siegel, K.

L. Carder, S. A. Garver, M. Kahru, and C. McClain.

1998: Ocean Color Chlorophyll Algorithms for SeaWiFS.

J. Geophys. Res. 103 (C11), 24,937-24,953.

O’Reilly, J. E. and others. 2000: Ocean Chlorophyll-a

Algorithms for SeaWiFS, OC2 and OC4: version 4.

SeaWiFS Postlaunch Calibration and Validation

Analyses, Part 3. Chapter 2. SeaWiFS Techinal Report.

(Submitted).

Thunell, R., R. Varela, M. Llano, J. Collister, F. MullerKarger, and R. Bohrer. 2000: Organic carbon flux in an

anoxic water column: sediment trap results from the

Cariaco Basin. Limnology and Oceanography. 45, 300-

308.
SIMBIOS Project Annual Report

107

This research was supported by the

SIMBIOS NASA contract # 97128

PEER REVIEWED PUBLICATIONS

Del Castillo, C., F. Gilbes, P. Coble, and F. E. Muller-Karger.

2000: On the dispersal of riverine colored dissolved

organic matter over the West Florida Shelf. Limnology

and Oceanography. Vol. 45, No. 6.

Hu, C., K. L. Carder, and F. E. Muller-Karger. 2000:

Atmospheric correction of SeaWIFS imagery over turbid

coastal waters; a practical method. Remote Sensing of

Environment. 74(2),195-206

Hu, C., K. L. Carder, and F. E. Muller-Karger. 2000:

Atmospheric correction of SeaWiFS imagery: assessment

of the use of alternative bands. Applied Optics,

39(21),3573-3581.

Hu, C., K. L. Carder, and F. Muller-Karger. 1998: Preliminary

algorithm to derive chlorophyll pigment concentration

and DOM absorption in turbid coastal waters from

SeaWiFS imagery. PORSEC Quingdao, China,

Proceedings, 888-892.

Hu, C., K. L. Carder, and F. Muller-Karger. 2000: A multiplatform approach to atmospheric correctionand

calibration of LANDSAT-7/ETM= imagery over aquatic

environments. Remote Sensing of Environment.

(Submitted ).

Hu, C., K. L. Carder. 2000: Atmospheric correction of

airborne sensors: comments on a scheme used for CASI.

Remote Sensing of Environment. (Submitted).

Hu, C., K. L. Carder, F. E. Muller-Karger. 2000: How precise

are SeaWiFS ocean color estimates? Implications from

digitization-noise errors. Remote Sensing of Environment,

(Submitted ).

Hu, C., F. E. Muller-Karger, D. C. Biggs, K. L. Carder, B.

Nababan, D. Nadeau, J. Vanderbloemen. 2000:

Comparison of ship and satellite bio-optical

measurements on the continental margin of the NE Gulf

of Mexico. International Journal of Remote Sensing,

(Submitted ).

Hu,C. F. E. Muller-Karger, S. Andrefouet, K. L. Carder, 2000:

A multi-platform approach to atmospheric correction and

calibration of LANDSAT-7/ETM+ imagery over aquatic

environments. Remote Sensing of Environment,

(Submitted ).

Melo Gonzalez, N.; Müller-Karger, F.E.; Cerdeira Estrada, S.;

Pérez de los Reyes, R.; Victoria del Rio, I.; Cárdenas

Perez, P.; Mitrani Arenal, I. 2000: Near-surface

phytoplankton distribution in the western Intra-Americas

Sea: The influence of El Niño and weather events.

Journal of Geophysical Research, Vol. 105. No. C6.

14029-14043.

Müller-Karger, F. E., and C. Fuentes-Yaco, 2000:

Characteristics of Wind-Generated Rings in the eastern

tropical Pacific Ocean. Journal of Geophysical Research.

Vol 105. C1. 1271-1284.

Muller-Karger, F. E. 2000: The Spring 1998 NEGOM Cold

Water Event: Remote Sensing Evidence for Upwelling

and for Eastward Advection of Mississippi Water (or:

How an Errant LC Anticyclone Took the NEGOM for a

Spin). Gulf of Mexico Science, 1. 55-67.

Muller-Karger, F. E. 2000: SMP workshop looks at role of

continental margins in ocean carbon cycle. In: U.S.

JGOFS Newsletter, Volume 10, Number 4, 4-6.

Muller-Karger, F., R. Varela, R. Thunell, M. Scranton, R.

Bohrer, G. Taylor, J. Capelo, Y. Astor, E. Tappa, T.-Y.

Ho, M. Iabichella, J. J. Walsh, and J. R. Diaz, 2000: The

CARIACO Project: Understanding the Link between the

Ocean Surface and the Sinking Flux of Particulate Carbon

in the Cariaco Basin. EOS. AGU Transactions. (In press)

Muller-Karger, F. E. R. Varela, R. Thunell, M. Scranton, R.

Bohrer, G. Taylor, J. Capelo, Y. Astor, E. Tappa, T. Y.

Ho, and J. J. Walsh, 2000: The Annual Cycle of Primary

Production in the Cariaco Basin: Implications for Vertical

Export of Carbon Along a Continental Margin. Journal of

Geophysical Research, (In press)

Neumann, A., R. Doerffer, H. Krawczyk, M. Dowell, R.

Arnone, C. Davis, M. Kishino, A. Tanaka, C. Hu, J.

Campbell, and S. Sathyendranath, 2000: Algorithm for

Case 2 waters”, IOCCG. Remote sensing of ocean color

in coastal, and other optically-complex, waters.

Sathyendranath, S. (eds), Reports of the International
SIMBIOS Project Annual Report

108

Ocean-Colour Coordinating Group, No. 3, IOCCG,

Dartmouth, Canada.

Perez, R., F. E. Muller-Karger, I. Victoria, N. Melo, S.

Cerdeira. 1999: Cuban, Mexican, US Researchers Probing

Mysteries of Yucatan Current. EOS. AGU Transactions.

Volume 80, No. 14, p. 153.

Perez, R., F. E. Muller-Karger, M. Merino, I. Victoria, N.

Melo, S. Cerdeira. 1999: Cuban, Mexican, US researchers

probing mysteries of Yucatan Current. Earth In Space.

Vol. 12, No. 1. p. 10-14.

Santamaría-del-Angel E., S. Alvarez-Borrego, R. MillanNuñez y F.E. Muller-Karger, 1999: On the weak effect of

summer upwelling on the phytoplankton biomass of the

Gulf of California. Rev.Soc.Mex.Hist.Nat., 49, 207-212.

Thunell, R., R. Varela, M. Llano, J. Collister, F. MullerKarger, and R. Bohrer. 2000: Organic carbon flux in an

anoxic water column: sediment trap results from the

Cariaco Basin. Limnology and Oceanography. 45, 300-

308.

Yao W., L.O. Hall, D.B. Goldgof, and F. Muller-Karger.

2000. Finding Green River in SeaWiFS Satellite Images.

Proceedings of the International Conference on Pattern

Recognition (ICPR'2000). September 3-8, 2000,

Barcelona, Spain.

Zhang M., L. Hall, D. Goldgof, and F. E. Muller-Karger.

2000: Knowledge-guided Classification of Coastal Zone

Color International Journal of Pattern Recognition and

Artificial Intelligence, (In press).

Zhang, M., K. Carder, Z. Lee, F. E. Muller-Karger, and D. B.

Goldgof. 1999: Noise Reduction and Atmospheric

Correction for Coastal Applications of Landsat Thematic

mapper Imagery. Remote Sensing of the Environment, 70,

167-180.

Zhang, M., L. O. Hall, D. B. Goldgof, and F. E. MullerKarger. 1997: Fuzzy Analysis of Satellite Images to Find

Phytoplankton Blooms. IEEE International Conference on

Systems, Man, and Cybernetics. Orlando, Florida.

Tang, D. L., I-H. Ni, D. R. Kester, and F. E. Müller-Karger, .

1999: Remote sensing observations of winter

phytoplankton blooms southwest of the Luzon Strait in

the South China Sea. Marine Ecology Progress Series, (in

press).

Thunell, R. E. Tappa., R. Varela, M. Llano, Y. Astor, F.

Muller-Karger, and R. Bohrer. 1999: Increased marine

sediment suspension and fluxes following an earthquake.

Nature, 398, 233-236.

PRESENTATIONS

Carder, K., C. Hu, and A. Strub, 1999: Application of a semianalytic SeaWiFS algorithm for chlorophyll-a and

Gelbstoff, American Society of Limnology and

Oceanography, Santa Fe, New Mexico.

Del Castillo, C. E., F. Gilbes, P. G. Coble, and F. E. MullerKarger. 1998: Optical Characteristics of Dissolved

Organic Matter During a Bloom on the West Florida

Shelf. Ocean Optics. Hawaii.

Hu, C., K. L. Carder, and F. Muller-Karger, 1998: Preliminary

algorith to derive chlorophyll pigment concentration and

DOM absorption in turbid coastal waters from SeaWiFS

imagery. PORSEC Quingdao, China, Proceedings, 888-

892.

Hu, C., F. Muller-Karger, K. L. Carder, and Z. Lee, 1998: A

method to derive optical properties over shallow waters

using SeaWiFS. SPIE Meeting, Kona, Hawaii.

Proceedings.

Hu, C., D. Biggs, D. Nadeau, and F. Muller-Karger, 1999:

Riverine impact on the distribution of chlorophyll-a and

colored dissolved organic matter (CDOM) in the

Northeastern Gulf of Mexico (NEGOM), American

Society of Limnology and Oceanography, Santa Fe, New

Mexico.

Hu, C., K. L. Carder, F. E. Muller-Karger, 1999: Digitization

errors in satellite ocean color data: implications based on

SeaWiFS. EOS, Transactions, American Geophysical

Union, 80(49), 154.

Hu, C., F. E. Muller-Karger, D. C. Biggs, B. Nababan, D.

Nadeau, 2000: Spatial and temporal variations of biooptical properties in the Northeastern Gulf of Mexico: two

year observations from ship and satellite, supplement to

EOS, Transactions, American Geophysical Union, 80(49),

33.

Perez, R., F. E. Muller-Karger, I. Victoria, N. Melo, S.

Cerdeira. 1999. Cuban, Mexican, US Researchers Probing

Mysteries of Yucatan Current. EOS. AGU Transactions.

Volume 80, No. 14, p. 153.
SIMBIOS Project Annual Report

109

Chapter 16

The Bermuda Bio-Optics Program (BBOP)

David A. Siegel

ICESS, University of California at Santa Barbara, Santa Barbara, California

16.1 INTRODUCTION

The Bermuda BioOptics Project (BBOP) is a collaborative

effort between the Institute for Computational Earth System

Science (ICESS) at the University of California at Santa

Barbara (UCSB) and the Bermuda Biological Station for

Research (BBSR). This research program is designed to

characterize light availability and utilization in the Sargasso

Sea, and to provide an optical link by which biogeochemical

observations may be used to evaluate bio-optical models for

pigment concentration, primary production, and sinking

particle fluxes from satellite-based ocean color sensors. The

BBOP time-series was initiated in 1992, and is carried out in

conjunction with the U.S. JGOFS Bermuda Atlantic Timeseries Study (BATS) at the Bermuda Biological Station for

Research. The BATS program itself has been observing

biogeochemical processes (primary productivity, particle flux

and elemental cycles) in the mesotrophic waters of the

Sargasso Sea since 1988. Closely affiliated with BBOP and

BATS is a separate NASA-funded study of the spatial

variability of biogeochemical processes in the Sargasso Sea

using high-resolution AVHRR and SeaWiFS data collected at

Bermuda (N. Nelson, P.I.). The collaboration between BATS

and BBOP measurements has resulted in a unique data set that

addresses not only the SIMBIOS goals but also the broader

issues of important factors controlling the carbon cycle.

16.2 RESEARCH ACTIVITIES

BBOP personnel participate on all BATS cruises, which

are conducted monthly with additional cruises during the

spring bloom period, January through May. Table 16.1

contains a list of data products collected by BBOP and/or

BATS, which are relevant to SIMBIOS. The BBOP project

collects continuous profiles of apparent optical properties

(AOPs) in the upper 200m and deployments are planned to

optimize match-ups with the BATS primary production

incubations and with SeaWiFS overpasses. The primary

optical measurements are downwelling vector irradiance and

upwelling radiance, Ed(z,t,λ) and Lu(z,t,λ), respectively.

Derived products include profiles of remote sensing

reflectance (Rrs(z,λ) and down- and upwelled attenuation

coefficients (Kd(z,λ), Ku(z,λ)), and are reported in near-real

time with ~98% reliability. From 1992 until mid-2000, the

primary profiling instrument has been a Biospherical

MER2040 (sn8728/8733). Recently these were replaced by a

free-falling Satlantic profiling radiometer system

(SPMR/SMSR s/n 028) which is also UV-capable. The

sampling package also includes a second mast-mounted

radiometer with wavebands matching those on the underwater

instrument for measuring incident downwelling vector

irradiance, Ed(0+,t,λ).

In 1994, collection of bottle samples for fluorometric

chlorophyll-a and inherent optical properties (IOPs) were

added. Chlorophyll-a is collected once or twice daily during

each cruise. Discrete samples for determining the absorption

spectra of particulates, aph(z,λ) and ad(z,λ), and CDOM

(ag(z,λ)) are collected according to Nelson et al (1998).

Particulate absorption spectra are determined using the

quantitative filter technique (Mitchell, 1990) and CDOM

absorption according to Nelson et al (1998). Hyperspectral

observations of above water R+

rs(λ) are collected using the

Analytical Spectral Devices FieldSpec spectrometer (ASD,

Boulder CO).

16.3 RESEARCH RESULTS

Careful attention to instrument behavior is crucial to

assuring high quality time-series data, and the diversity of

instruments used by BBOP during the past year has lead to

new challenges in instrument calibration and intercomparison.

To date, the largest source of calibration error that we have

found is variability between individual calibration lamps and

different calibration of the same lamp. Since the radiometers

used in our projects have been remarkably stable, we have

been able to observe the 1% variability between calibration

lamps stated by the manufacturer (O'Brien et al in press).

Thus, we have concluded that calibration coefficients are best

calculated from a long-term average of several calibrations

using several different lamps. Following the procedures that

have proved fruitful with our Biospherical radiometers, we

have embarked on a detailed description of the calibration

behavior of our SPMR systems. Our recently purchased

NIST-calibrated lamp should reduce inter-calibration

variability by a significant amount. Our calibration facility

regularly conducts informal comparisons between

manufacturers’ calibrations and our own and has achieved

good agreement. For example, a recent check of the
SIMBIOS Project Annual Report

110

calibration of a BBSR SPMR/SMSR (s/n 028) yielded results

within 2% of the calibration done 3 months earlier by the

manufacturer (D. Menzies, pers. comm.). In addition, the field

comparisons between this Satlantic radiometer and MER

8728/8733 have been quite good (Fig. 16.1), even though

these instruments were calibrated at different facilities.

The Sargasso Sea near Bermuda is mesotrophic,

characterized by both eutrophic and oligotrophic conditions

during different times of the year. Low chlorophyll-a stocks

and primary production rates prevail for the most of the year,

although there is a short spring bloom characterized by

somewhat higher concentrations and rates. The accepted

mechanisms of nutrient supply have accounted for less than

half of the annual nutrient flux required to fuel these blooms.

Using evidence from BATS-CTD surveys, moored

instruments, satellite altimetry and eddy-resolving model

simulations, Siegel, et al (1999) and McGillicuddy et al (1998)

illustrated the importance of mesoscale eddies to new

production in the Sargasso Sea. They demonstrated that eddy

pumping and entrainment into the mixed layer during winter

convection are the two dominant mechanisms transporting

new nutrients into the euphotic zone at BATS. Smaller

contributions are made by mixing in the thermocline and

wind-driven transport, effectively balancing geochemical

estimates of annual new production.

The problems associated with modeling integrated

primary production and effective quantum yield (φc, mol C

Ein-1) in the Sargasso Sea have been illustrated using sitespecific and previously published global models and a 6-year

time series of BBOP and BATS data (Sorensen and Siegel in

press, Siegel et al in press). The low predictive capability of

published models using a variety of parameters demonstrates

that we have yet to develop a predictive understanding of the

important photophysiological, ecological and methodological

processes controlling primary production. In addition, the

assumptions of steady state and balanced growth required by

production models are not easily met by the “snapshot” nature

of optical or satellite measurements, and boundaries must be

placed on how empirical models for integrated primary

production are developed, validated and applied to satellite

ocean color data sets. Although the values of φc were highly

variable over time, few parameters correlated consistently and

rationally with φc. One parameter with an interesting negative

correlation was the concentration of photoprotective

carotenoids and xanthophylls. The concentration of

photoprotective carotenoids relative to chlorophyll is an

indicator of phytoplankton species composition with

implications for light absorption which may be related to

systematic changes in φc values.

Phytoplankton fluorescence has been proposed as a means

of assessing biomass and primary production rates in

laboratory, in situ, and in satellite based investigations. A

time series of the quantum yield of phytoplankton

fluorescence (φf) from BBOP was used to predict depthdependent and depth-integrated primary production rates

(Westberry and Siegel, in prep). Preliminary results give

fluorescence quantum yields (φf) similar to previous studies (1

to 5%), and ratios of φc/φf that vary from ~ 0 to 5 atoms carbon

fixed per quanta fluoresced. The predictions of integrated

primary productivity have been marginally successful (r2

 <=

50%), although they are comparable with results from global

models for this region. Improvements have been hindered by

weak statistical relationships with other physical and

biological parameters. Seasonal variations in φf showed

maximum values in the surface layer during summer months

and lower, more uniform values throughout the winter when

deep mixing occurs. Episodic changes in φf during the

summer were associated with shoaling of isopycnals and may

be attributed to the ‘eddy pumping’ mechanism. These results

suggest that determinations of φf may be useful for assessing

the nutritional status of phytoplankton populations.

The time-series of CDOM measurements (now in its sixth

year) has revealed significant inter-annual fluctuations in the

'background' concentration of CDOM upon which the

previously described annual cycle of CDOM is superimposed

(Fig. 16.2, Nelson et al. 1998). Periods of low background

CDOM concentration and high CDOM spectral slope appear

to lag behind periods of high-salinity anomalies at the BATS

site, which may suggest a role for CDOM as a semiconservative tracer of surface water masses. This time series

has also confirmed previous inferences made from BBOP

AOP profiles (e.g. Siegel and Michaels 1996), including a

seasonal time-scale for the bleaching rates of CDOM.

Preliminary results of another collaborative study (NSFfunded) suggest that the higher concentration of CDOM in

surface waters during winter and spring offsets the lower

seasonal light availability relative to the summer, leading to

similar summer/winter-spring photochemical reaction rates

(Nelson and Carlson in prep). Our study of the sources and

sinks of CDOM in the Sargasso Sea (also collaborative)

suggests an important role for zooplankton and microbial

community in the annual cycle of CDOM.

The importance of CD(O)M to light penetration the

Sargasso Sea is also evident in the absorbance profiles

collected with the WET Labs ac9 at the BATS station from

1994-1997 (Fig. 16.2, Brody et al. in prep). The seasonal

patterns in absorbance observed with the ac9 were consistent

with other optical descriptions of the Sargasso Sea (Siegel et

al 1995b, Siegel and Michaels 1996, Nelson et al 1998).

During the winters when the water column was well mixed,

absorbance is approximately uniform throughout the water

column. During periods of spring and summer stratification,

values at the surface and at depth were lower (e.g. less than

0.01 m-1 at 440nm) and a seasonal maximum was observed

near 100m. These maxima were coincident with chlorophyll

peaks. The lower absorbance values at the surface during the

summer have been observed in several studies, and have been

hypothesized to be the result of decreased phytoplankton

stocks after nutrient depletion or due to bleaching (Siegel and

Michaels 1996, Nelson et al 1998). A weak exponential

relationship existed between Chl-a and a440 in the surface layer
SIMBIOS Project Annual Report

111

and near the chlorophyll max (r2

 = 0.58 and 0.50, respectively,

n=248). Below 150m, published models did not fit the data at

all, and usually underestimated observed absorbance. If the

data were subsampled to include only data points in which

CDM was known to be low and uniform (i.e., in the surface

layer during winter-early spring), the fit could be improved to

about 80%. During the fall, when absorbance by CDM has

been observed to be highest (Nelson, et al 1998), the

chlorophyll-based model underestimated absorbance

throughout the water column. Our ac9 data set supports the

suggestion that simple chlorophyll-based models are

inappropriate for the open ocean, and leave the optical

definition of Case I waters open to question.

REFERENCES

Brody, E.A., D.A. Siegel , M.C. O’Brien, S. Wolfe, J.R.

Morrison and N.B. Nelson, in prep: Variability of in situ

inherent optical properties in the Sargasso Sea. For

Limnol. Oceanogr.

Mitchell, B.G., 1990: Algorithms for determining the

absorption coefficient for aquatic particles using the

quantitative filter technique. Ocean Optics X,

Proceedings of S.P.I.E., 1302, 137-142.

McGillicuddy Jr., D.J., A.R. Robinson, D.A. Siegel, H.W.

Jannasch, R. Johnson, T.D. Dickey, J. McNeil, A.F.

Michaels and A.H. Knap, 1998: Influence of mesoscale

eddies on new production in the Sargasso Sea. Nature,

394, 63-265.

Nelson N.B. and C. Carlson (in prep). Microbial production

of chromophoric dissolved organic matter in the Sargasso

Sea.

Nelson, N.B., D.A. Siegel, and A.F. Michaels, 1998: Seasonal

dynamics of colored dissolved material in the Sargasso

Sea, Deep Sea Research I, 45, 931-957.

Siegel, D.A., E. Fields and D.J. McGillicuddy, Jr., 1999:

Mesoscale motions, satellite altimetry and new production

in the Sargasso Sea. Journal of Geophysical Research,

104, 13359-13379.

Siegel, D.A., T.K. Westberry, M.C. O'Brien, N.B. Nelson,

A.F. Michaels, J.R. Morrison, E.A. Caporelli, J.C.

Sorensen, S.A. Garver, E.A. Brody, J. Ubante and M.A.

Hammer, in press: The Bermuda BioOptics Project: Biooptical modeling of primary production from spacesensible variables. In press Deep Sea Research II.

Sorensen J. and D.A Siegel, in press. Variability and models

of the effective quantum yield of carbon assimilation in

the Sargasso Sea. In press Deep Sea Research II.

Westberry TK. and D.A. Siegel (in prep). Solar-stimulated

fluorescence and primary production in the Sargasso Sea.

Figure 16.1 Comparison of average reflectance (Lu/Ed) in the top 20m from the BBOP Biospherical MER-2040 s/n 8733 (×),

and the BBSR Satlantic SPMR s/n 028 (), during February 2000.
SIMBIOS Project Annual Report

112

Figure 16.2 Bio-optical properties of the BATS site, 1996-1999. A: chlorophyll a concentration, showing the permanent

subsurface chlorophyll maximum layer and the spring bloom, with depth of 1% light penetration (dotted line). B: Diffuse

attenuation coefficient (Kd, m-1) at 490 nm, an apparent optical property closely related to particulate light absorption, also with

Z1%PAR (note the similarity in distribution to chlorophyll a). C: Ratio of Kd(410 nm) to Kd(443 nm), highlighting the optical

implications of changes in CDM (colored dissolved and detrital matter) distribution. D: Absorption coefficient of CDOM

(colored dissolved organic matter, a subset of CDM) at 300 nm, from spectroscopic measurements of filtered seawater

samples; panels C and D also show mixed-layer depth.
SIMBIOS Project Annual Report

113

TABLE 16.1. A partial list of measurements made by BBOP & BATS

BBOP

Direct Measurements:

Ed(z,λ) Downwelling vector irradiance (410,441,465,488,510,520,555,565,589,625,665 & 683 nm)

Ed(0+,λ) Incident irradiance (340,390,410,441,465,488,520,545,565,589,625,665 & 683 & 350-1050 nm)

Lu(z,λ) Upwelling radiance (410,441,465,488,510,520,555,565,589,625,665 & 683 nm)

atp(λ) Particulate absorption spectrum by QFT

ad(λ) Detrital particle absorption spectrum by MeOH extraction

ays(λ) Colored dissolved absorption spectrum

Eo(z,λ) Scalar irradiance at 441 and 488 nm

Ff(z) Natural chlorophyll fluorescence using a broadband upwelled radiance sensor

chl-fl(z) Chlorophyll fluorescence with a SeaTech fluorometer

c(z,660) Beam attenuation coefficient at 660 nm with SeaTech 25 cm transmissometer

T(z) & S(z) Temperature and conductivity with SeaBird probes

chl-a(z) Discrete chlorophyll a determinations via Turner fluorometry (for next day delivery)

Primary Derived Products:

LwN(λ) Normalized water leaving radiance (410,441,465,488,510,520,555,565,589,625,665 & 683 nm)

RRS(0-,λ) In-water remote sensing reflectance (410,441,465,488,510,520,555,565,589,625,665 & 683nm)

RRS(0+,λ) Above-water remote sensing reflectance (350 to 1050 nm)

aph(λ) Phytoplankton absorption spectrum (= ap(λ) - adet(λ))

Kd(z,λ) Attenuation coefficient for Ed(z,λ) (410,441,465,488,510,520,555,565,589,625,665 & 683 nm)

KL(z,λ) Attenuation coefficient for Lu(z,λ) (410,441,465,488,510,520,555,565,589,625,665 & 683 nm)

<PAR(z)> Daily mean photosynthetically available radiation at depths of the in situ C14 incubations

b(z,λ) Spectral scattering coefficient (= c(z,λ) - a(z,λ))

U.S. JGOFS BATS (NSF) and Related Biogeochemistry Sampling Programs

Primary Production (in situ 14C incubation) Sinking flux (sediment trap array)

Phytoplankton pigments (fluorometric & HPLC) Nutrients (NO3+NO2, SiO4, PO4)

CO2 system (alkalinity, TCO2 and pCO2) Continuous atmosphere & surface pCO2

Dissolved oxygen (continuous & discrete) Zooplankton biomass & grazing

POC & PON (POP infrequently) DOC & DON (DOP infrequently)

Full water column, WOCE-standard CTD profile Bacterial abundance and rates

Phytoplankton abundance by flow cytometry Coccolithophore abundance

Validation spatial cruises (5 days, 4cruises/year) Deep ocean sediment sinking rates
SIMBIOS Project Annual Report

114

This research was supported by the

SIMBIOS NASA contract # 97125

PEER REVIEWED PUBLICATIONS

Dickey, T., S. Zedler, D. Frye, H. Jannasch, D. Manov, D.

Sigurdson, J. D. McNeil, L. Dobeck, X. Yu, T. Gilboy, C.

Bravo, S. C. Doney, D.A. Siegel and N. Nelson: Physical

and biogeochemical variability from hours to years at the

Bermuda Testbed Mooring: June 1994 - March 1998. Deep

Sea Research, Part II.

Dickey, T., D. Frye, J. McNeil, D. Manov, N. Nelson, D.

Sigurdson, H. Jannasch, D. Siegel, T. Michaels and R.

Johnson, 1998: Upper ocean response to Hurricane Felix

as measured by the Bermuda Testbed Mooring. Monthly

Weather Review, 126, 1195-1201.

Dickey, T., D. Frye, H. Jannasch, E. Boyle, D. Manov, D.

Sigurdson, J. McNeil, M. Stramska, T. Michaels, N.

Nelson, D. Siegel, G. Chang and J. Woo, 1998:

Preliminary results from the Bermuda testbed mooring

program. Deep Sea Research I, 45, 771-794.

Garver, S.A., and D.A. Siegel, 1997: Inherent optical property

inversion of ocean color spectra and its biogeochemical

interpretation: I. Time series from the Sargasso Sea.

Journal of Geophysical Research., 102, 18,607-18,625.

McGillicuddy, Jr., D.J., A.R. Robinson, D.A. Siegel, H.W.

Jannasch, R. Johnson, T.D. Dickey, J. McNeil, A.F.

Michaels and A.H. Knap, 1998: Influence of mesoscale

eddies on new production in the Sargasso Sea. Nature,

394, 263-265.

McGillicuddy, Jr., D.J., R.J. Johnson, D.A. Siegel, A.F.

Michaels, N. Bates and A.H. Knap, 1999: Mesoscale

variability of ocean biogeochemistry in the Sargasso Sea.

Journal of Geophysical Research, 104, 13381-13394.

Nelson, N.B., D.A. Siegel, and A.F. Michaels, 1998: Seasonal

dynamics of colored dissolved material in the Sargasso

Sea. Deep Sea Research I, 45, 931-957.

Nelson, N.B., N.R. Bates, D.A. Siegel, and A.F. Michaels;

Spatial variability of the CO2 sink in the Sargasso Sea.

Deep-Sea Research, Part II.

O'Reilly, J.J., S. Maritorena, D.A. Siegel et al: Ocean Color

Chlorophyll a Algorithms for SeaWiFS,OC2 and OC4:

Version 4. NASA Tech. Memo., SeaWiFS Postlaunch

Calibration and Validation Analyses, Vol. 3, Chap. 2.

O'Reilly, J., S. Maritorena, B.G. Mitchell, D.A. Siegel, K.L.

Carder, M. Kahru, S.A. Garver, C.R. McClain, 1998:

Ocean color algorithms for SeaWiFS. Journal of

Geophysical Research, 103, 24,937-24,953.

Siegel, D.A, and W.G. Deuser, 1997: Trajectories of sinking

particles in the Sargasso Sea: Modeling of statistical

funnels above deep-ocean sediment traps. Deep-Sea

Research, I , 44, 1,519-1,541.

Siegel, D.A., E. Fields and D.J. McGillicuddy, Jr., 1999:

Mesoscale motions, satellite altimetry and new production

in the Sargasso Sea. Journal of Geophysical Research,

104, 13359-13379.

Accepted

Sorensen, J.C., and D.A. Siegel, 1999: Variability and models

of the effective quantum yield of carbon assimilation in

the Sargasso Sea, Accepted for publication in Deep Sea

Research, II.

Submitted

Behrenfeld, M.J., E. Marañon, D.A. Siegel and S.B. Hooker:

Modeling oceanic primary production: Photoacclimation

and nutrient effects on light-saturated photosynthesis.

Submitted to Limnology and Oceanography.

Siegel, D.A., S. Maritorena, N.B. Nelson, D.A., Hansell and

M. Lorenzi-Kayser. Global Distribution of Colored

Dissolved Organic Matter. Submitted to Global

Biogeochemical Cycles.

Toole, D.A., D.A. Siegel, D.W. Menzies, M.J. Neumann and

R.C. Smith, 1999: Remote sensing reflectance

determinations in coastal environments - Impacts of

instrumental characteristics and environmental variability.

In review in Applied Optics.
SIMBIOS Project Annual Report

115

Siegel, D.A., M.C. O'Brien, T.K. Westberry, N.B. Nelson,

A.F. Michaels, J.R. Morrison, E.A. Caporelli, J.C.

Sorensen, S.A. Garver, E.A. Brody, J. Ubante and M.A.

Hammer, 1999: The Bermuda BioOptics Project: Biooptical modeling of primary production from spacesensible variables. Submitted to Deep Sea Research, II.

Siegel, D.A., D.M. Karl and A.F. Michaels: Interpretations of

biogeochemical processes from the U.S. JGOFS Bermuda

and Hawaii time series sites. Deep-Sea Research, Part II.

Siegel, D.A., T.K. Westberry, M.C. O'Brien, N.B. Nelson,

A.F. Michaels, J.R. Morrison, A. Scott, E.A. Caporelli,

J.C. Sorensen, S. Maritorena, S.A. Garver, E.A. Brody, J.

Ubante & M.A. Hammer: Bio-Optical Modeling of

Primary Production on Regional Scales: The Bermuda

BioOptics Project. Deep-Sea Research, Part II.

Siegel, D.A., M. Wang, S. Maritorena and W. Robinson.

2000. Atmospheric correction of satellite ocean color

imagery: the black pixel assumption. Applied Optics, 39,

3582-2591.

OTHER PUBLICATIONS

O'Brien, M.C., D.W. Menzies, D.A. Siegel and R.C. Smith,

1999, Calibration History of Several Marine

Environmental Radiometers. SeaWiFS Post Launch

Technical Report Series, NASA Tech. Memo. 1999-

206892. In press.

PRESENTATIONS

Brody, E.A., D.A. Siegel, M.C. O'Brien, E.A. Caporelli and

N.B. Nelson. 1997: Temporal patterns of IOPs in the

Sargasso Sea. Presented at the 1997 ALSO Aquatic

Sciences Meeting. Sante Fe.

Fields, E.A., D.A. Siegel and D.J. McGillicuddy, 1998:

Mesoscale motions, satellite altimetry and

biogeochemical processes in the Sargasso Sea. Presented

at the AGU Ocean Sciences Meeting. San Diego, Trans.

Amer, Geophys. Union, Eos, 79, no. 1, OS22D-9.

Garver, S.A., and D.A. Siegel, 1997: Global Application of a

nonlinear inherent optical property inversion model.

Presented at the 1997 ALSO Aquatic Sciences Meeting.

Sante Fe.

Michaels, A.F., D.A. Siegel, R. Johnson, N. Nelson, N. Bates

and A. Knap, 1998: Hydrostation S, the Bermuda Atlantic

Time-series Study and decadal variability in the Sargasso

Sea. Presented at the AGU Ocean Sciences Meeting. San

Diego. Trans. Amer, Geophys. Union, Eos, 79, no. 1,

OS21I-4.

Nelson, N.B., C.A. Carlson, N.L. Krupp, and D.A. Siegel,

1997: Dynamics of chromophoric dissolved organic

material (CDOM) in the Sargasso Sea. Presented at the

1997 ALSO Aquatic Sciences Meeting. Sante Fe.

Nelson, N.B., D.A. Siegel and J.A. Yoder, 2000: The spring

bloom in the Sargasso Sea: Spatial pattern and relationship

to winter mixing Talk and abstract presented at the 2000

AGU Ocean Sciences Meeting (OS12M-10), January

2000, San Antonio TX.

O'Brien, M.C., D. Menzies, D.A. Siegel and R.C. Smith, 1998:

Long-term calibration history of several marine

environmental radiometers. Presented at the AGU Ocean

Sciences Meeting. San Diego.. Trans. Amer, Geophys.

Union, Eos, 79, no. 1, OS32A-2.

Peterson, A.R, D.A. Siegel, S.A. Maritorena and S.A. Garver,

1999: Optimal tuning of semi-analytical ocean color

algorithms for the global ocean. Presented at the 1999

ASLO Aquatic Sciences Meeting, Sante Fe.

Scott, A. J. and N.B. Nelson, 2000: Light energy available for

photochemistry in the Sargasso Sea, August 1999. Poster

presented at the 2000 AGU Ocean Sciences Meeting

(OS31A-11), January 2000, San Antonio TX.

Shih ,A.S., D.A. Siegel, N.B. Nelson, M.C. O'Brien and S.A.

Garver, 1998: Modeling of optical property

characteristics: Analyses of observations from the

Sargasso Sea. Presented at the AGU Ocean Sciences

Meeting. San Diego, Trans. Amer, Geophys. Union, Eos,

79, no. 1, OS12B-9.

Siegel, D.A, E. Fields, D.J. McGillicuddy and A.F. Michaels,

1997: Mesoscale motions, satellite altimetry and

biogeochemical processes in the Sargasso Sea. Presented

at the 1997 ALSO Aquatic Sciences Meeting, Sante Fe.

Siegel, D.A., 1998: Spectral data assimilation for merging

satellite ocean color imagery, Presented at the Ocean

Optics XIV Meeting, Kona HI.

Siegel, D.A., 1999: Orthothogonal views of the global ocean

biosphere using SeaWiFS. Presented at the 1999 ASLO

Aquatic Sciences Meeting, Sante Fe.

Siegel, D.A., M. Lorenzi-Kaiser, and S. Maritorena, 1999:

Alternative views of the global ocean biosphere using

SeaWiFS. Opening Science Address, SeaWiFS Science

Team Meeting, Moss Landing, CA, May 1999.

Siegel, D., J. Yoder, S. Doney, N. Nelson, M. Kennelly, M.

Lorenzi-Kaiser, 1999: Satellite Views of the North

Atlantic Spring Bloom, To be presented at 1999 AGU

Fall Meeting.
SIMBIOS Project Annual Report

116

Westberry, T.K., D.A. Siegel, N.B. Nelson and M.C. O'Brien,

1999: Regional modeling of primary production in the

Sargasso Sea. Presented at the 1999 ASLO Aquatic

Sciences Meeting, Sante Fe.

Westberry, T.K., D.A. Siegel, 2000: Solar-stimulated

fluorescence variability in the Sargasso Sea. Poster and

abstract presented at the 2000 AGU Ocean Sciences

Meeting (OS22B-02), January 2000, San Antonio TX.
SIMBIOS Project Annual Report

117

Chapter 17

Spectral Data Assimilation for Merging Satellite Ocean Color

Imagery

David A. Siegel and Stéphane Maritorena

ICESS, University of California at Santa Barbara, Santa Barbara, California

17.1 INTRODUCTION

The Sensor Intercomparison and Merger for Biological

and Interdisciplinary Oceanic Studies (SIMBIOS) project was

initiated "to develop a methodology and operational capability

to combine data products from various ocean color missions in

a manner that ensures the best possible global coverage and

best exploits the complementary missions of the sensors"

(GSFC ocean color group, 1995). While the merging of data

from multiple sources has already been implemented for SST

(e.g. Reynolds & Smith, 1994), altimetry (e.g. Le Traon &

Ogor, 1998) or clouds (Rossow & Schiffer, 1991), this is a

new topic in ocean color science.

 Merging ocean color data has obvious advantage in terms

of spatial and temporal coverage of the global ocean (see

Gregg et al., 1998; Gregg and Woodward, 1998) but data

merging should also result in new, more diverse and improved

data products with lower uncertainties. The merging of ocean

color satellite data is generally considered at the level of the

global gridded products, i.e. NASA Level-3 (IOCCG, 1999)

and, more specifically, for the merging of chlorophyll a

concentration data, [Chl]. This approach limits computational

issues and deals with a unique, simple quantity, [Chl].

However, ocean color data merging can also be conducted

at the level of water-leaving radiances, LwN(λ), using either

empirical or semi-analytical algorithms. Although more

challenging, this latter approach is potentially extremely

powerful as it can achieve good consistency in the final data

products and can take advantage of both the specificities and

differences of each source of data. The demonstration of the

feasibility of such approach is the main objective of our

SIMBIOS work.

Here, we use a semi-analytical algorithm (Garver &

Siegel, 1997; Maritorena et al., 2000) to merge Rrs (or,

equivalently, LwN) data from different sources to estimate

[Chl]. The model also allows for the retrieval of other

quantities such as the combined absorption of colored detrital

particulate and dissolved organic matter at 443 nm, acdm(443)

and the particulate backscattering, bbp(443).

The model is designed to deal with the uncertainties

associated with each source of input data and to allow the

calculation of uncertainty estimates of the retrieved products.

Examples are illustrated using the SeaWiFS matchup data set

(Bailey et al., 2000). An example of the benefit of using data

sources with different wavebands is also presented.

17.2 RESEARCH ACTIVITIES

The UCSB IOP Inversion Model for Ocean Color

Over the past years, our group has developed and

validated a semi-analytical, inherent optical property (IOP)

inversion model for ocean color that can be applied to satellite

ocean color data (Garver and Siegel, 1997; Maritorena et al.,

2000). The functional relationship between LwN(λ) and the

backscattering to absorption ratio is taken from Gordon et al.

(1988) and the IOP spectra, a(λ) and bb(λ), are partitioned

into relevant components of seawater backscatter, bbw(λ) and

absorption, aw(λ), particulate backscatter, bbp(λ),

phytoplankton absorption. aph(λ), and the combined of

dissolved and detrital particulate absorption coefficients,

acdm(λ). Values of bbw(λ) and aw(λ) are assumed to be

known (e.g., Smith and Baker, 1981; Pope and Fry, 1997).

The contributions to total absorption by detrital

particulates and dissolved materials are lumped together into a

single term, acdm(λ), as these factors are essentially

indistinguishable (Carder et al., 1991, Nelson et al., 1998).

Each of the non-water terms are parameterized as a constant

spectral shape scaled by an unknown magnitude. These

magnitudes, namely the chlorophyll a concentration ([Chl]),

the CDM absorption coefficient (acdm(λo)), and the

particulate backscatter coefficient (bbp (λo)), for aph(λ),

acdm(λ), and bbp(λ) respectively, are the unknowns in the

model. The reference wavelength (λo) is 443 nm. A complete

formulation of the model is as follows:
SIMBIOS Project Annual Report

118

where S is the spectral decay constant for CDM absorption

(Bricaud et al. 1981, Green & Blough, 1994), n is power law

exponent for particulate backscattering coefficient, t is the seaair transmission factor, F0 is the extraterrestrial solar

irradiance and m is the index of refraction of the water. By

determining LwN(λ) (or Rrs(λ)) for more than 3 wavelengths,

non-linear least-squares techniques can be used to solve for

the unknowns in equation (17.1). For later purposes, the

equation 19.1 can be stated as ˆ

L wN (λ;Θ;Ψ) where Θ is the

vector for the retrieved parameters (Θ = [[Chl], acdm(λo) and

bbp(λo)), Ψ is the vector of the model parameters (Ψ = [ap

*

h(λ1) ... ap

* h(λN) S n]). The parameters to retrieve, Θ, are

obtained by minimizing a cost function we defined to be the

mean square difference between modeled and measured data,

or

CF = 

1

(Nλ-1) ∑

i=1

Nλ

 [ ˆ

L wN (λi; Θ ;Ψ) - LwN(λi)]

2

 (17.2)

where Nλ is the number of wavelengths available and LwN(λi)

is the measured normalized water-leaving radiance for

wavelength i. For the merging of data from several sources,

the cost function can be summed over the various data sources

(see below). Several numerical methods exist to solve eq. 17.1

using the cost function in eq. 17.2. In its present state, the

model uses a nonlinear Levenberg-Marquardt fitting

procedure. One advantage of this method is that it can take

into account the uncertainties associated with the data injected

in the model.

Global Optimization of the UCSB IOP Inversion Model Using

Simulated Annealing

In its initial development, the various terms forming a and

bb in the UCSB model were based on formulations and

coefficients taken from the literature. These initial versions of

the model were reasonably good but, as revealed during

SeaBAM (O'Reilly et al., 1998), they were not performing as

well as some other models or empirical algorithms. Improving

the overall performance of the UCSB IOP model was thus a

necessary step before it could be used for data merging. For

this purpose, we have developed a procedure based on the

simulated annealing technique (Press et al., 1992) that

optimizes the vector of parameters, Ψ, in the UCSB IOP

model so it returns the best possible retrievals under most

situations. This is not a trivial task because of the high nonlinearity of model (eq. 17.1) and the large number of

(potentially interacting) parameters for which optimal values

need to be determined. The method has first been tested

successfully on synthetic data (Peterson et al., 1999). We have

then developed a "semi-real" data set (i.e. actual [Chl] and Rrs

measurements and theoretically derived acdm(443) and bbp(443)

using an “improved” version of the SeaBAM data containing

diffuse attenuation coefficient data (Kd)) which we used in the

optimization procedure. The [Chl] retrievals generated by the

model after the optimization are excellent (Maritorena et al.,

2000), with performance as good as those of the SeaWiFS

operational algorithm (Figure 17.1).

Figure 17.1 Chlorophyll retrievals using an “improved” SeaBAM data set. Left panel : [Chl] retrievals using the optimized UCSB IOP

inversion model as a function of in situ [Chl]. Right panel: [Chl ]retrievals using OC2v4 algorithm.

i n 2

0 bw bp 0 0

wN 2 n

i=1 bw bp 0 0 w ph cdm 0 0

*

t F b ( ) + b ( )( / ) ˆ

L ( ) = gi (17.1)

m b ( ) b ( / ) a ( ) [Chl]a ( ,[Chl]) a ( ) exp( S( ))

λ λ λλ

λ

λ λ λλ λ λ λ λ λ

−

−

+ + + + −−

      ∑ ( )
SIMBIOS Project Annual Report

119

Results for the other retrieved variables (acdm(443) and

bbp(443)) are also very good although these results still need to

be fully validated against actual in situ measurements.

The SeaWiFS Matchup Data Set

To develop, test and demonstrate the feasibility of our

approach for data merging, we used the SeaWiFS matchup

data set which is assembled and maintained by the SeaWiFS

project (Bailey et al., 2000). This data set contains nearly

simultaneous in situ and SeaWiFS measurements at the same

location. The data set we used contains 126 stations from

various optical provinces and chlorophyll concentrations

ranging from ~0.04 to ~5 mg m-3. Figure 17.2a shows the

[Chl] retrievals from the UCSB IOP model when using the

SeaWiFS Rrs data from the matchup data set. While most of

the points are reasonably close to the 1:1 line several of them

are strongly underestimated by the model. For some stations,

the model returned either a negative [Chl] or failed to

converge during the non-linear fitting process. These low

estimates and failures are likely a consequence of the

sensitivity of the model to any kind of "noise" in the data, and

particularly in the short wavelengths bands. Sensitivity to

noise is a typical problem with semi-analytical algorithms.

Data merging along with the weighting of the data according

to their estimated level of uncertainty, can correct for these

problems.

Merging the In-situ Data and SeaWiFS Data.

Whether the UCSB IOP model uses one or several

sources of data, it solves for the unknowns by minimizing, in

the nonlinear fit, the cost function described in eq. 17.2. Under

this simple scheme, the in situ and SeaWiFS data from the

matchup set can be used together, as input to the model. The

results of this in situ and SeaWiFS data merging are illustrated

in figure 17.2b. Compared to the results with the SeaWiFS

data alone (fig. 17.2a), the statistical results are clearly

improved (better slope, better R2

, smaller bias) with the

merged data. Data merging also results in fewer negative or

non-converging retrievals (1 over 126 stations) but for the

sake of comparison with non-merged data these new points

were not included in the analysis.

With the merged data, the statistical results are clearly

better (better slope, better R2

, smaller bias, higher number of

stations successfully processed) than with the SeaWiFS data

alone. In this example, both sources of data were given the

same weight in the merging process, that is each source of

data had a similar influence in the minimization of the cost

function. This approach is fine when the uncertainties

associated with each of the data sources are of the same order

of magnitude. In other cases, a weighting of the input data is

recommended.

Weighting of the Input Data by their Uncertainty Level

The cost function used in the UCSB IOP model (eq. 17.2)

can actually account for the uncertainties associated with the

input data by dividing the difference between model and

observed quantities by the uncertainty estimate for the

observation (e.g., Press et al. 1992), as

where Nλi

 are the number of wavebands for data source i, Nsat

is the number of observing platforms providing data, LwNi(λj) is the observed LwN for band j of data source i,

ˆ

L wN (λ;Θ;Ψ) is the modeled LwN and σi(λj) is the uncertainty

level for band j from source i. The uncertainty weighting in

equation 3 insures that the best observations have a higher

weight in the inversion for the retrieval vector, ˆ

θ .

Following the work of Hooker & Maritorena (2000), the

level of uncertainty for in situ data should be close to 5% for

the the visible bands. Assuming a 20% uncertainty in Rrs in

each of the SeaWiFS visible bands leads to the [Chl] retrievals

presented in figure 19.3. The overall benefit of the weighting

by the uncertainty level is obvious with an improvement of all

statistics.

In the case study presented here, the uncertainties in the

input data were set to the same value for all the wavebands

(and all the stations) of a particular data source but each band

and each station can actually handle specific settings. For

example, at short wavelengths in satellite data (e.g. the 412 nm

band) where perturbation from atmospheric correction can be

important (see e.g. Siegel et al., 2000), it is reasonable to

assume a larger level of uncertainty than in other bands and

thus to limit the influence of these perturbations in the

inversion of the model. Assessing the level of uncertainty for

each band of a specific spaceborne sensor is not a

straightforward problem. Vicarious calibrations and

intercomparison studies conducted in the frame of the

SIMBIOS Project will likely provide the necessary

information on the accuracy of the sensors bands.

Uncertainties in Data Products

The UCSB IOP inversion model provides uncertainty

estimates for its outputs using a linear approximation to the

calculation of non-linear regression inference regions (Bates &

Watts, 1988).

sat -i

2

N N

wN j wN-i j

i = 1 j = 1 i j

L ( , , ) - L ( ) ˆ

CF= (17.3) ( )

λ λ λ

σ λ

  Θ Ψ  

  ∑∑
SIMBIOS Project Annual Report

120

Figure 17.2. (a) Chlorophyll retrievals using the UCSB IOP model with the SeaWIFS data from the

matchup data set as a function of in situ [Chl]. The 1:1 (solid) and regression (dotted) lines are also

plotted. (b) Chlorophyll retrievals from the UCSB IOP model using both the SeaWIFS and in situ Rrs

data from the matchup data set as a function of in situ [Chl].

Figure 17.3. [Chl] retrievals from the UCSB IOP model using both SeaWiFS and in situ Rrs data with assumed

levels of uncertainty of 20% and 5%, respectively. The 1:1 (solid) and regression (dotted) lines are also plotted.

This approach can also be used when merging data. The

standard error for each estimate is given by the square root of

the total mean square error between observed Rrs and modeled

Rrs (using the retrieved IOPs, i.e. forward model) multiplied

by the corresponding diagonal element of the inverted Z

matrix where Z = transpose(V)*V and V is the matrix of

partial derivatives of the UCSB IOP model for the

wavelengths of a particular Rrs spectrum. In the linear

approximation, this latter term corresponds to the ratio of the

parameter plane area to that of the expectation plane (Bates

and Watts, 1988) :

se(i) = mse * inverse(transpose(V) * V)[i, i]

Uncertainties (standard errors) in [Chl] retrievals are

plotted on figure 17.4a when using the SeaWiFS data alone

and the same stations are plotted when merged with in situ

data (fig. 17.4b; assuming a 5% uncertainty at all wavelengths
SIMBIOS Project Annual Report

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for in situ Rrs data and 20% for SeaWIFS). In Figure 17.4a,

uncertainties are generally large, sometimes of same order of

magnitude (or larger !) than the estimated [Chl]. Uncertainties

are much reduced with the merged data although a few

stations (6 over 101) still show high levels of uncertainty in

their estimated [Chl].

Figure 17.4. [Chl] retrievals and standard errors from the UCSB IOP model. (a) using SeaWiFS data only. (b)

using SeaWiFS and in situ Rrs data from the matchup data set (assuming the same level of uncertainty in the

data sources than in figure 17.3). When the standard error is greater than the estimated [Chl], the lower error

bar is not plotted.

Figure 17.5 Estimated [Chl] and standard errors from the UCSB IOP model. Left panel : using a data source with

Rrs at 412, 490, 520 and 565nm. Center panel : using a data source with Rrs at 412, 443, 470, 510 and 555nm. Right

panel : using the merged data. When the standard error is greater than the estimated [Chl], the lower error bar is not

plotted.

Merging data with different bands

Because we used the SeaWiFS matchup data set to develop

and test our approach of data merging, the previous examples

were based on the use of data sources with the same bands

(the 5 first SeaWiFS bands). The UCSB IOP inversion model

is however designed to handle data sources having different

bands. This is an important aspect as the various ocean color
SIMBIOS Project Annual Report

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sensors to be used within the next decade will have different

bands. The potential benefits of using data sources with

different bands can be illustrated by the merging of 2 sets of in

situ data with different bands using the UCSB model. To do

so, the Rrs data collected with an in situ radiometer during a

recent cruise were split into 2 different files , one containing

the 412, 490, 520 and 565nm channels (source 1) and the

second receiving the 412, 443, 470, 510 and 555 nm data

(source 2). The results (for [Chl]) using each of the data sets

separately and merged are shown on figure 19.5. The results

with source 1 only clearly show a good slope and R2

 but the

bias is important in the estimated [Chl] and the uncertainties

are sometimes high. With the data from source 2, the results

are better although the slope of the regression between the

estimated and measured [Chl] is relatively far from 1.0. With

the merged data, all statistical parameters are improved with a

good slope, high R2

, small bias and reduced uncertainties.

17.3 CONCLUSIONS

In this study, we have shown the feasibility of using an

ocean color semi-analytical algorithm to merge ocean color

radiometric data from different sources to estimate the

concentration of chlorophyll a. Data merging leads to better,

more accurate retrievals with lower uncertainties. The merging

procedure we have developed also deals with uncertainties

associated with the input data and, provided that these

uncertainties are known with sufficient accuracy, uses them to

weight the contribution of each source of data in the merging

process. This allows more weight to be given to the "best" data

when inverting the model to derive the estimated [Chl] and

IOPs. The model also calculates the uncertainties for each of

the retrieved parameters.

Using in situ and SeaWiFS data, we have shown some of

the benefits that can be expected from data merging. We have

also shown how the use of data sources with different bands

can improve the overall results of the merging. The same

benefits should result from the merging of data from 2 or more

satellite. We are planning on testing our procedure to merge

SeaWiFS and MODIS data in a near future. To merge

different satellite data we will also develop a BRDF correction

scheme (in collaboration with A. Morel) to take the geometry

of the data sources into account. We also plan on improving

several aspects of the model itself. The major improvement we

are currently working on is a "bandless" formulation for

absorption that will allow the model to work with virtually any

possible wavebands. In this report we have mostly

documented the results for the [Chl] estimated by the model

while other variables (i.e. acdm(443) and bbp(443)) are also

retrieved. These other quantities are important for the ocean

color science (see Siegel et al., 2000) and data merging should

also result in increased accuracy of these data products.

REFRENCES

Bailey, S.W., C.R.McClain, P.J. Werdelland B.D. Schieber.

2000: Normalized Water-Leaving Radiance and

Chlorophylla Match-Up Analyses SeaWiFS Postlaunch

Technical Report Series Vol. 10, NASA Tech. Memo.

2000-206892, S.B. Hooker and E.R. Firestone, Eds.,

NASA Goddard Space Flight Center, Greenbelt,

Maryland, (In press).

Bates, D.M., and D.G. Watts, 1988: Nonlinear Regression

Analysis and its Applications. Wily, 365 pp.

Bennett, A.F., 1992: Inverse Methods in Physical

Oceanography, Cambridge, 453 pp.

Bricaud, A., A. Morel and L. Prieur, 1981: Absorption by

dissolved organic matter in the sea (yellow substance) in

the UV and visible domains. Limnology and

Oceanography, 26, 43-53.

Bricaud, A., M. Babin, A. Morel and H. Claustre. 1995:

Variability in the chlorophyll-specific absorption

coefficients of natural phytoplankton: analysis and

parameterization, J. Geophys. Res., 100(C7): 13321-

13332.

Bricaud, A, A. Morel, M. Babin, K. Allali, H. Claustre. 1998:

Variations of light absorption by suspended particles with

chlorophyll a concentration in oceanic (case 1) waters:

Analysis and implications for bio-optical models. J.

Geophys. Res., 103, (C13) 31033-31044.

Carder, K.L., S.K. Hawes, K.A. Baker, R.C. Smith, R.G.

Steward, and B.G. Mitchell, 1991: Reflectance model for

quantifying chlorophyll a in the presence of productivity

degradation products. J. Geophys. Res., 96, 20,599-

20,611.

Garver, S.A., and D.A. Siegel, 1997: Inherent optical property

inversion of ocean color spectra and its biogeochemical

interpretation: I. Time series from the Sargasso Sea. J.

Geophys. Res., 102, 18,607-18,625.

Gordon, H.R., O.B. Brown, R.H. Evans, J.W. Brown, R.C.

Smith, K.S. Baker, and D.K. Clark. 1988: A semi-analytic

radiance model of ocean color. J. Geophys. Res., 93,

10,909-10,924.

Green S.A., Blough N.V. 1994: Optical-absorption and

fluorescence properties of chromophoric dissolved

organic-matter in natural-waters. Limnol. Oceanogr, 39:

(8), 1903-1916.
SIMBIOS Project Annual Report

123

Hooker S.B. and S. Maritorena. 2000: An Evaluation of

Oceanographic Radiometers and Deployment

Methodologies. J. Atmos. Ocean. Tech., 17(6) : 811-830.

Gregg, WW; Esaias, WE; Feldman, GC; Frouin, R; Hooker,

SB; McClain, CR; and Woodward, RH. 1998: Coverage

opportunities for global ocean color in a multimission era.

IEEE Transactions On Geoscience And Remote Sensing,

36 :1620-1627.

Gregg, WW and Woodward, 2000: RH. Improvements in

coverage frequency of ocean color: Combining data from

SeaWiFS and MODIS. IEEE Transactions On Geoscience

And Remote Sensing, 36, 1350-1353.

IOCCG. 1999: Status and Plans for Satellite Ocean-Colour

Missions: Considerations for Complementary Missions.

Yoder, J.A. (ed.), Reports of the International OceanColour Coordinating Group, No. 2, IOCCG, Darmouth,

Canada, 43pp.

Le Traon, P.Y. and F. Ogor, 1998: ERS-1/2 orbit

improvement using TOPEX/POSEIDON: The 2cm

challenge. J. Geophys. Res., 103, 8045-8057.

Maritorena, S., D.A. Siegel, A.R. Peterson and M. LorenziKayser, 2000: Tuning of a pseudo-analytical ocean color

algorithm for studies at global scales. Talk and abstract

presented at the 2000 AGU Ocean Sciences Meeting

(OS12M-05), San Antonio Texas.

Morel A. and S. Maritorena, 2000: Bio-optical properties of

oceanic waters : a reappraisal. In Press J. Geophys. Res.

Nelson, N.B., Siegel D.A. and Michaels A.F. 1998. Seasonal

dynamics of colored dissolved material in the Sargasso

Sea. Deep-Sea Res. I, 45: (6), 931-957.

O'Reilly, J., S. Maritorena, B.G. Mitchell, D.A. Siegel, K.L.

Carder, M. Kahru, S.A. Garver, C.R. McClain, 1998:

Ocean color algorithms for SeaWiFS. J. Geophys. Res.,

103, 24,937-24,953.

Parker, R.L., 1994; Geophysical Inverse Theory, Princeton

Univ. Press, 386 pp.

Peterson, A.R, D.A. Siegel, S.A. Maritorena and S.A. Garver,

1999: Optimal tuning of semi-analytical ocean color

algorithms for the global ocean. Presented at the 1999

ASLO Aquatic Sciences Meeting, Sante Fe, NM, Feb.

1999.

Pope, R.M., and E.S. Fry, 1997: Absorption spectrum (380-

700 nm) of pure water. 2. Integrating cavity

measurements. Applied Optics, 36, 8710-8723.

Press W.H., S.A. Teukolsky, W.T. Vettering and B.P.

Flannery, 1992: Numerical Recipes in C: The Art of

Scientific Computing, 2nd edition. Cambridge University

Press, 994 pp.

Reynolds, R.W., and T.M. Smith, 1994: Improved global sea

surface temperature analyses using optimum

interpolation. J. Climate, 7, 929-948.

Rossow, W.B., and R.A. Schiffer, 1991: ISCCP cloud data

products. Bull. Amer. Met. Soc., 72, 2-20.

Siegel D. A., S. Maritorena, N. B. Nelson, D.A. Hansell and

M. Lorenzi-Kayser, 2000: Global Distribution of Colored

Dissolved Organic Materials. Global Biogeochemical

Cycles, (Submitted).

Siegel, D.A., M. Wang, S. Maritorena and W. Robinson.

2000: Atmospheric correction of satellite ocean color

imagery: the black pixel assumption. Applied Optics, 39 ,

3582 - 3591.

Smith, R.C., and K.S. Baker, 1981: Optical properties of the

clearest natural waters. Applied Optics, 20, 177-184.

This research was supported by the

SIMBIOS NASA contract # 98083

PEER REVIEWED PUBLICATIONS

Hooker S.B. and S. Maritorena. 2000. An Evaluation of

Oceanographic Radiometers and Deployment

Methodologies. J. Atmos. Ocean. Tech., 17(6): 811-

830.

Maritorena, S., A. Morel and B. Gentili. Determination

of the fluorescence quantum yield by oceanic

phytoplankton in their natural habitat. Applied

Optics (In Press).
SIMBIOS Project Annual Report

124

Morel A. and S. Maritorena. Bio-optical properties of

oceanic waters : a reappraisal. J. Geophys. Res. (In

Press).

Siegel, D.A., M. Wang, S. Maritorena and W. Robinson.

2000. Atmospheric correction of satellite ocean

color imagery: the black pixel assumption. Applied

Optics, 39 : 3582 - 3591.

Siegel, D.A., 1998: Spectral data assimilation for

merging satellite ocean color imagery. Extended

abstract published on the CD proceedings from

Ocean Optics XIV.

PRESENTATIONS

Maritorena, S., D.A. Siegel, A.R. Peterson & M.

Lorenzi-Kayser, 2000: Tuning of a pseudoanalytical ocean color algorithm for studies at global

scales. AGU Ocean Sciences Meeting (OS12M-05),

San Antonio.

Peterson, A.R, D.A. Siegel, S.A. Maritorena and S.A.

Garver, 1999: Optimal tuning of semi-analytical

ocean color algorithms for the global ocean. ASLO

Aquatic Sciences Meeting, Sante Fe.

Siegel, D.A., 1998: Spectral data assimilation for

merging satellite ocean color imagery, Ocean

Optics XIV meeting, Kona. Extended abstract

published on the CD proceedings from Ocean

Optics XIV.

Siegel, D.A., 1999: Orthothogonal views of the global

ocean biosphere using SeaWiFS. ASLO Aquatic

Sciences Meeting, Sante Fe.
SIMBIOS Project Annual Report

125

Chapter 18

SIMBIOS Data Product and Algorithm Validation with

Emphasis on the Biogeochemical and Inherent Optical

Properties

J. Ronald V. Zaneveld, W. Scott Pegau, and Andrew H. Barnard

College of Oceanic and Atmospheric Sciences, Oregon State University, Corvallis, Oregon

18.1 INTRODUCTION

The purpose of our component of the SIMBIOS

program is to address and quantify the relative

accuracy of the various ocean color remote sensing

products by means of product and algorithm validation.

In order to accomplish these goals, we have been

collaborating in an international research program in

the Gulf of California designed to examine the spatial

and temporal bio-optical variability in this region. In

addition, we participated in various cruises of

opportunity to supplement our validation data set. Our

long-term objectives are to; a) collect optical and

biochemical data in oceanic and coastal regions of

interest, b) provide collected data to the SeaBASS

database, the SIMBIOS project office, and other

interested users, c) maintain and update

instrumentation and data archiving and dissemination

systems, and d) determine spatial and temporal error

fields for the biological and geophysical data products

from the various ocean color missions.

18.2 RESEARCH ACTIVITIES

Over the past 6 years we have been involved in a

multi-national research effort focused on determining

the physical and biogeochemical variability in the Gulf

of California. This research is done in coordination

with Drs. J. Mueller and C. Trees from San Diego State

University (SDSU), Drs. S Alvarez Borrego, R. LaraLara, G. Gaxiola and H. Maske from the Centro de

Investigacion Cientifica y de Educacion Superor de

Ensenada (CICESE), Ensenada, Mexico, and Dr. E.

Valdez from the University of Sonora, Hermosillo,

Mexico. Our component has been to measure the

inherent optical properties (IOP; the absorption,

scattering, and attenuation characteristics of the water

and the materials suspended and dissolved therein) and

apparent optical properties (AOP; the radiometric

parameters of the submarine daylight field) as well as

the traditional CTD parameters on vertical scales less

than 0.5 m.

During the three-year contract period, we have

participated in 9 field campaigns, collecting an

extensive amount of radiometric, inherent optical

property, aerosol optical thickness, and physical data

(Table 18.1). The field campaigns included cruises

offshore of Oregon, 5 Gulf of California cruises

(Figure 1), a cruise with Dr. Francisco Chavez

(MBARI), a cruise of opportunity with Drs. Vivian

Montecino (University of Chile) and Giovanni Daneri

(University of Vaparaiso), and the MODIS validation

cruise off Southern California, the West coast of the

Baja peninsula in Mexico, and the Gulf of California

(MOCE5). We have maintained and utilized the

optical absorption/attenuation profiling system that is

part of the SIMBIOS instrument pool. We also

maintained 3 water filtration systems as a part of the

SIMBIOS equipment pool. Recently, we have

developed a self-recording small inherent optical

property, CTD and radiometer profiling system for

small boat operations. In the past year we have worked

extensively on building an inline optical sampling

system to be deployed on a commercial ferry in the

Gulf of California.

Two sampling platforms were typically used

during these cruises; a SLOW Descent Rate Optical

Profiler (SLOWDROP) to measure the inherent optical

properties as well as the physical parameters, and a

profiling radiometer system to measure the

downwelling irradiance and upwelling radiance.

Typically, SLOWDROP and radiometer profiles are

made within an hour of each other at the same location.

The SLOWDROP platform is free-falling and

slightly negatively buoyant providing 10-20 cm scale

vertical resolution. A typical instrument configuration

includes a SeaBird SBE-25 CTD for measuring

physical parameters, a fluorometer, and two WETLabs

ac-9 (9 wavelength absorption and attenuation meters)

for measuring the particulate and dissolved

components of the IOP. In 1998, a HOBI Labs
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Figure 18.1. Map of the Gulf of California with sampling locations from the 8 cruises conducted.
SIMBIOS Project Annual Report

127

Hydroscat-6 was added to the platform to measure the

spectral backscattering coefficient. CTD and IOP data

are collected simultaneously during a single profile

using a WETLabs MODAPS system for data collection

and integration. Calibrations of the ac-9 instruments

were performed several times during the contract

period. Field calibrations of the ac-9 meters were

typically done once per sampling day using a clean

water standard produced using a Barnstead nanopure

water system according to the SIMBIOS protocols for

this instrument.

A Satlantic Inc. SPMR system was used to

measure profiles of the apparent optical properties

including downwelling irradiance and upwelling

radiance at 412, 444, 490, 533, 555, 590, and 684 nm.

The Satlantic radiometer is calibrated at SDSU by Drs.

Mueller and Pegau following NIST protocols and

calibrated twice a year by Satlantic Inc. The optical

filters on this radiometer were upgraded by the

manufacturer (Satlantic) over the past year.

One meter binned profiles of all of the data, were

provided to the SeaBASS database within 90 days of

completion of each cruise. Also provided are the

calibration histories of the ac-9's and the radiometer, as

well as a detailed logbook of the cruise activities. The

dates of each SIMBIOS-supported cruise and the

number of profiles made using each system are listed in

Table 18.1. Figure 18.1 shows the station locations

sampled during each cruise within the Gulf of

California.

Currently, we are receiving all of the SeaWiFS

HRPT Level 1A data for the West Coast of North

America including the Gulf of California from the

DAAC. This data has been archived and processed to

various levels for use in the validation of ocean color

products.

We have maintained an optical profiling system

for use in the SIMBIOS instrument pool. The system

contains 2 hyperspectral absorption and attenuation

meters (WETLabs Histars), a CTD (SeaBird

Electronics 25), a data acquisition system (WETLabs

MODAPS+), cage, and necessary cabling. A full time

technician was hired to accompany this system, to

assist users in deployment and operation and to process

the data. We also maintained 3 deionizing water

filtration systems (Barnstead Nanopure) which are for

use by the SIMBIOS authorized users. These systems

are used to produce a clean water standard for ac-9 and

HiStar field calibrations.

 We conducted two ac9 workshops designed to

train technicians and scientists how to operate the

instruments and interpret the data, and to compare

techniques used by various groups. In attendance were

40 people representing 5 European programs, NASA,

NRL, NavOc, NDBC, and numerous research groups.

We found that most programs followed the same

operating protocols with a few minor exceptions.

Many of these exceptions will be written into future

operating protocols. The group recommended testing

the use of a longer reference wavelength in the ac9.

Those tests are underway at OSU.

We have also been involved in a collaborative

effort with Drs. Knut Stamnes and Bingquan Chen at

the Stevens Institute of Technology investigating

atmospheric correction of SeaWiFS imagery.

18.3 RESEARCH RESULTS

Data collected in the Gulf of California as well as

other locations were used to examine the spectral

relationships between the inherent optical properties in

Barnard et al. (1998a). The results of this research

were used to compare and contrast the inherent optical

properties observed in the Gulf of California (Pegau et

al. 1998). In the shallow northwest region of the Gulf,

Case II waters are consistently observed due to the

resuspension of sediments. In the central portion of the

Gulf, the largest variability in optical properties is

associated with coastal upwelling. Away from the

coasts the spectral optical properties are fairly constant.

Validation of the normalized water leaving

radiance measured by SeaWiFS in the Gulf of

California during the 1997 and spring 1998 cruises was

examined in Barnard et al. (1998b). The results of this

research indicated that atmospheric correction was

problematic, often causing negative water leaving

radiance at 412 nm.

In coordination with our SeaWiFS funding, we

have undertaken the development of a new remote

sensing algorithm involving the triple ratio of the

remote sensing reflectance. The results of this work

were presented at the 1998 Ocean Sciences meeting at

San Diego, CA in a presentation titled "A nearly

backscattering independent algorithm for the retrieval

of spectral absorption from remote-sensing reflectence"

by J. R. V. Zaneveld, A. H. Barnard, and W. S. Pegau.

This work was subsequently published in Applied

Optics (Barnard et al., 1999a).

We have also compared the in situ radiance with

the upwelling radiance resolved by the OCTS sensor

off the northeastern U.S. coast. The results of this work

were presented at the 1998 Ocean Sciences meeting in

a talk titled "Bio-optical maps derived from ocean

color imagery using a nearly backscattering

independent algorithm" by A. H. Barnard, J. R. V.

Zaneveld, and W. S. Pegau.

A detailed analysis of the spectral characteristics

of the absorption, attenuation, and scattering data

collected in the Gulf of California over the past 5 years

has been completed (Pegau, et al., 1998; 1999; 2000).

In situ data collected in the Gulf of California during
SIMBIOS Project Annual Report

128

two separate research cruises, were used in an

investigation into the validation of SeaWiFS satellite

measurements (Barnard, et al., 1998). Radiometric

data collected during a recent cruise in the Gulf of

California were used to validate the normalized water

leaving radiance estimated by the SeaWiFS satellite.

The results of this work show that the blue wavelengths

are greatly underestimated by the SeaWiFS sensor

possibly due to inaccurate atmospheric correction

(Figure 18.2).

In order to correctly invert the remotely sensed

reflectance to obtain the optical properties, we

developed a method for the appropriate depth

averaging of remotely sensed optical parameters

(Zaneveld, et al., 1998). This method was used to

investigate the influence of vertical structure on the

remotely sensed signal. The results of this work were

presented at the ASLO Aquatic Sciences meeting in

Santa Fe, NM, February 1-5, 1999, titled "Assessing a

key implicit assumption in ocean color remote sensing

algorithms: Optical homogeneity" (Barnard, et al,

1999b).

Based on the consistent relationships resolved by

an earlier study (Barnard, et al, 1998), we developed a

method to obtain closure between the inherent optical

properties and remotely sensed reflectance

measurements (Barnard, et al., 1999a). The results of

this model showed that inversion of the remotely

sensed reflectance to obtain the absorption coefficient

is possible if the spectral relationships of the absorption

coefficient are known. A recent model that resulted

from this research was the determination of

photosynthetically available radiation light levels using

profiles of the absorption coefficient (Barnard, et

al.,1999c).

As a result of our participation in the MODIS

validation cruise, we have been working with Dr.

Dennis Clark of NOA to achieve closure between his

IOP and AOP measurements.

REFERENCES

Barnard, A. H., W. S. Pegau, and J. R. V. Zaneveld,

1998a: Global relationships of the inherent optical

properties of the oceans, J. Geophys. Res., 103,

24,955-24,968.

Barnard, A. H., J. R. V. Zaneveld, J. L. Mueller, W. S.

Pegau, and C. Trees, 1998b: SeaWiFS ocean color

in the Gulf of California: Variability, validation,

and algorithm development, Ocean Optics XIV

Conference Papers, Kailua-Kona, HI, November

10-13.

Barnard, A.H., J.R.V. Zaneveld, and W.S. Pegau.

1999a: Remotely sensed reflectance and the

absorption coefficient: Closure and inversion.

Applied Optics, 38, 5108-5117.

Barnard, A.H., J.R.V. Zaneveld, W.S. Pegau, and T.J.

Cowles. 1999b: Assessing a key implicit

assumption in ocean color remote sensing: Optical

homogeneity. ASLO Aquatic Sciences meeting,

Santa Fe,.

Barnard, A.H., J.R.V. Zaneveld, W.S. Pegau, J.L

Mueller, H. Maske, R. Lara-Lara, S. AlvarezBorrego, and E. Valdez-Holquin. 1999c: The

determination of PAR levels from absorption

coefficient profiles at 490 nm. Ciencias Marinas.

25(4), 487-507.

Pegau, W.S., A.H. Barnard, J.R.V. Zaneveld, J.L.

Mueller, H. Maske, R. Lara-Lara, S. AlvarezBorrego, E. Valdez-Holguin, and R. CervantesDuarte. 1998: Optical variability in the Gulf of

California. Ocean Optics XIV Conference Papers,

Volume 2, Kailua-Kona, Hawaii.

Pegau, W.S., J.R.V. Zaneveld, A.H. Barnard, H.

Maske, S. Alvarez-Borrego, R. Lara-Lara, R.

Cervantes-Duarte. 1999: Inherent optical

properties in the Gulf of California. Ciencias

Marinas. 25(4), 469-485.

Zaneveld, J.R.V., A.H. Barnard, W.S. Pegau, and E.

Boss. 1998: An investigation into the appropriate

depth average of remotely sensed optical

parameters. Ocean Optics XIV Conference Papers,

Volume 2, Kailua-Kona, Hawaii.
SIMBIOS Project Annual Report

129

Figure 18.2 - Spectral comparison of the normalized water leaving radiance measured in situ (black) and as determined by

SeaWiFS using the Gordon and Wang (blue), Siegel (green) and Stumpf (red) atmospheric correction algorithms for 5 days of

match-up data in the Gulf of California (April 21, 24, 26, 28, and 30, 1999).

-1.2

-0.8

-0.4

0

0.4

0.8

1.2

400 440 480 520 560

Wavelength (nm)

nLw (μW/cm2/sr)

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

400 440 480 520 560

Wavelength (nm)

nLw (

μW/cm2/sr)

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

400 440 480 520 560

Wavelength (nm)

nLw (

μW/cm2/sr)

0.1

0.2

0.3

0.4

0.5

0.6

0.7

400 440 480 520 560

Wavelength (nm)

nLw (μW/cm2/sr)

0.1

0.2

0.3

0.4

0.5

0.6

0.7

400 440 480 520 560

Wavelength (nm)

nLw (μW/cm2/sr)
SIMBIOS Project Annual Report

130

Table 18.1 - Dates and number of profiles made during each of the SIMBIOS cruises. The first two listed cruises were not

supported by SIMBIOS, but the data has been submitted to SeaBASS. In additionally discrete samples for pigment analyses

were taken, and sun photometer readings were obtained.

Cruise Name Dates # IOP profiles # AOP profiles

Gulf Of California 1995

fall

11/25/95 to 12/6/95 28 69

Gulf Of California 1996

fall

10/30/96 to 11/7/96 25 25

Gulf Of California 1997

fall

10/16/97 to 10/29/97 39 24

Offshore Oregon 1997 9/6/97 to 9/18/97 150 0

Gulf of California 1998

spring

3/6/98 to 3/16/98 31 11

Gulf of California 1998

fall

11/27/98 to 12/7/98 36 19

Coastal California 5/22/99 to 5/27/99 6 0

Coastal Chile 7/4/99 to 7/17/99 22 22

Gulf of California 1999

spring

4/21/99 to 5/1/99 40 19

Gulf of California 1999

fall

10/29/99 to 11/7/99 37 23

Modis validation cruise

(MOCE5)

10/1/99 to 10/21/99 48 (carried out by other

researchers)

This research was supported by the

SIMBIOS NASA contract # 97129

PEER REVIEWED PUBLICATIONS

Barnard, A.H., W.S. Pegau, and J.R.V. Zaneveld. 1998:

Global relationships in the Inherent Optical Properties of

the oceans. J. Geophys. Res., 103:24,955-24,968.

Barnard, A.H., J.R.V. Zaneveld, and W.S. Pegau, 1999:

Remotely sensed reflectance and the absorption

coefficient: Closure and inversion. Applied Optics, (In

press).

Barnard, A.H., J.R.V. Zaneveld, W.S. Pegau, J.L Mueller, H.

Maske, R. Lara-Lara, S. Alvarez-Borrego, and E. ValdezHolquin, 1999: The determination of PAR levels from

absorption coefficient profiles at 490 nm. Ciencias

Marinas. (In press).

Pegau, W.S., J.R.V. Zaneveld, A.H. Barnard, H. Maske, S.

Alvarez-Borrego, R. Lara-Lara, R. Cervantes-Duarte.

1999: Inherent optical properties in the Gulf of

California, Ciencias Marinas. (In press).

Zaneveld, J.R.V and W.S. Pegau. 1998: A model for the

reflectance of thin layers, fronts, and internal waves and

its inversion. Oceanography. 11: 44-47.

PRESENTATIONS

Barnard, A.H., J.R.V. Zaneveld, and W.S. Pegau, 1997:

Comparison of inherent optical properties across a variety

of oceanic regimes for application to remote sensing.

ALSO Aquatic Sciences, Sante Fe.

Barnard, A.H., J.R.V. Zaneveld, and W.S. Pegau, 1998: Biooptical maps derived from ocean color imagery using a

backscattering independent algorithm. AGU/ASLO 1998

Ocean Sciences Meeting, San Diego.

Barnard A.H., J.R.V. Zaneveld, and W.S. Pegau, 1998:

SeaWiFS ocean color in the Gulf of California:

Validation. Ocean Optics XIV Conference Papers,

Volume 2, Kailua-Kona, HI.
SIMBIOS Project Annual Report

131

Barnard, A. H., J. R. V. Zaneveld, W. S. Pegau, and T. J.

Cowles. 1999: Assessing a key implicit assumption in

ocean color remote sensing algorithms: Optical

homogeneity. ASLO Aquatic Sciences Meeting, Sante

Fe.

Mueller, J.L., J.R.V. Zaneveld, W.S. Pegau, E. Valdez, H.

Maske, S. Alvarrez-Borrego, and R. Lara-Lara. 1997:

Remote sensing reflectance: preliminary comparisons

between in-water and above-water measurements, and

estimates modelled from measured inherent optical

properties. SPIE, 2963: 502-513.

Pegau, W.S., A.H. Barnard, J.R.V. Zaneveld, J.L. Mueller, H.

Maske, R. Lara-Lara, S. Alvarez-Borrego, E. ValdezHolguin, and R. Cervantes-Duarte. 1998: Optical

variability in the Gulf of California. Ocean Optics XIV

Conference Papers, Volume 2, Kailua-Kona, HI.

Zaneveld, J.R.V., W.S. Pegau, A.H. Barnard, J.L. Mueller, H.

Maske, E. Valdez, R. Lara-Lara, and S. Alvarez-Borreo.

1997: Prediction of euphotic depths and diffuse

attenuation coefficients from absorption profiles: A

model based on comparison between vertical profiles of

spectral absorption, spectral irradiance, and PAR. SPIE,

2963: 585-590.

Zaneveld, J.R.V, A.H. Barnard, and W.S. Pegau. 1998: A

nearly backscattering independent algorithm for the

retrieval of spectral absorption from remote-sensing

reflectance. AGU/ASLO 1998 Ocean Sciences Meeting,

San Diego.

Zaneveld, J.R.V., A.H. Barnard, W.S. Pegau, and E. Boss.

1998: An investigation into the appropriate depth average

of remotely sensed optical parameters. Ocean Optics XIV

Conference Papers, Volume 2, Kailua-Kona, HI.
SIMBIOS Project Annual Report

132

Chapter 19

Aerosol Optical Properties over the Oceans: Summary and

Interpretation of Shadow-Band Radiometer Data from Six

Cruises

Mark A. Miller, R. M. Reynolds and Mary Jane Bartholomew

Brookhaven National Laboratory Upton, New York

19.1 INTRODUCTION

The aerosol scattering component of the total radiance

measured at the detectors of ocean color satellites is

determined with atmospheric correction algorithms. These

algorithms are based on aerosol optical thickness

measurements made in two channels that lie in the nearinfrared portion of the electromagnetic spectrum. The aerosol

properties in the near-infrared region are used because there is

no significant contribution to the satellite-measured radiance

from the underlying ocean surface in that spectral region. In

the visible wavelength bands, the spectrum of radiation

scattered from the turbid atmosphere is convolved with the

spectrum of radiation scattered from the surface layers of the

ocean. The radiance contribution made by aerosols in the

visible bands is determined from the near-infrared

measurements through the use of aerosol models and radiation

transfer codes. Selection of appropriate aerosol models from

the near-infrared measurements is a fundamental challenge.

There are several challenges with respect to the

development, improvement, and evaluation of satellite oceancolor atmospheric correction algorithms. A common thread

among these challenges is the lack of over-ocean aerosol data.

Until recently, one of the most important limitations has been

the lack of techniques and instruments to make aerosol

measurements at sea. There has been steady progress in this

area over the past five years, and there are several new and

promising devices and techniques for data collection. The

development of new instruments and the collection of more

aerosol data from over the world’s oceans have brought the

realization that aerosol measurements that can be directly

compared with aerosol measurements from ocean color

satellite measurements are difficult to obtain. There are two

problems that limit these types of comparisons: the cloudiness

of the atmosphere over the world’s oceans and the limitations

of the techniques and instruments used to collect aerosol data

from ships. To address the latter, we have developed a new

type of shipboard sun photometer.

There are two primary types of sun photometer used to

measure the aerosol optical depth: narrow field-of-view

radiometers aimed at the solar disk and rotating shadow-band

radiometers. Shadow-band radiometers are wide-field-ofview radiometers that employ an occulting apparatus to add

directional capabilities. Just as narrow-field-of-view

radiometers must be accurately aimed, a serious drawback for

measurements on ships at sea, conventional shadow-band

radiometers also require exact orientation. Fast-Rotating

Shadow-band Radiometers (FRSRs) are a hybrid form of the

original shadow-band radiometer design, but do not have a

rigid requirement for orientation. Thus, it is possible to use a

slightly modified version of the land-based FRSR on ships.

As compared to land-based FRSRs, shipboard FRSRs require

much faster rotation of the occulting arm, faster-response

silicon detectors, higher data sampling rates, and more

sophisticated data analysis.

As part of our NASA SIMBIOS contract and with

additional support from the Department of Energy’s (DOE)

Atmospheric Radiation Measurement (ARM) Program, we

developed and deployed a new instrument during the past two

years: the shipboard Fast-Rotating Shadow-band Spectral

Radiometer (FRSR; Reynolds et al., 1999). This instrument

makes continuous, semi-automated shipboard measurements

of the direct-normal, diffuse, and global irradiance in seven

channels (415 nm, 500 nm, ~610 nm, ~660 nm, ~862 nm, 936

nm, and broadband) and does not require a mechanically

stabilized platform, thereby making it cost effective and

reliable. The FRSR can operate continuously for months on a

ship and can be monitored by someone with minimal training.

The FRSR is part of a Portable Radiation Package (PRP),

which also contains broadband solar and IR Eppley

radiometers. The PRP has been deployed extensively during

the past two years, traversing parts of all three oceans, and has

demonstrated stable calibration. It has moved from proof-ofconcept to a nearly operational instrument.

The shipboard FRSR measures spectral global and diffuse

irradiance with 2-minute resolution. These data are combined

with information about the pitch, roll, and heading of the ship

to calculate the 2-minute spectral direct-normal irradiance.

From these data, the aerosol optical thickness can be

computed during clear periods using the Langley regression

technique, or continuously if high quality extraterrestrial

calibration coefficients are known. In addition, the diffuse

irradiance measured by the FRSR can be used to evaluate
SIMBIOS Project Annual Report

133

fractional cloudiness, thereby providing a means to test cloudfiltering algorithms used in satellite ocean color retrievals.

Our strategy for collecting relevant aerosol data from over

the oceans has evolved over the past three years. As a

benchmark of our progress, we (our group at the Brookhaven

National Laboratory) believe that we have at least doubled the

number of shipboard aerosol measurements over the world’s

oceans, and possibly tripled it. We have collected aerosol data

from all three oceans and the database is growing rapidly.

Coincidentally, our ability to correctly interpret these aerosol

measurements is being refined. In this report, we present

summary results from six separate deployments of the FRSR.

We also discuss our data processing strategy, especially our

uncertainty analyses, and comment on plans for the coming

three years.

19.3 RESEARCH ACTIVITIES

Evaluating Ocean Color Atmospheric Correction Algorithms

An aerosol correction algorithm was developed by

Gordon (1978), applied to the problem of phytoplankton

pigment retrieval by Gordon and Clark (1981), and used in the

CZCS by Gordon et al. (1980). Mathematically, the

atmospheric correction parameters, (, ) i j ελλ , for the Gordon

scheme are given by

0

0

( ) ( ) (, , ) (, ) , ( ) ( ) (, , )

aia i a i

i j

a ja j a j

p

p

ω λτ λ θθ λ ελλ

ω λ τ λ θθ λ = (19.1)

where ( ) ω λ a i is the aerosol single scattering albedo,

( ) a τ λ is the aerosol optical thickness, and 0 (, , )i p θθ λ is the

aerosol scattering phase function. The Gordon technique is to

model (, ) i j ελλ using a power-law relationship defined as

(, ) .

n

i

i j

j

λ ελλ

λ

−

 

=      

(19.2)

The coefficient n is determined for two wavelengths in

the near infrared and used to select an aerosol model. From

this model, radiation transfer codes are used to estimate the

satellite radiance contribution of the aerosol in the visible

wavelengths (Gordon and Wang, 1994). Evaluation of this

atmospheric correction scheme therefore involves collection

of shipboard data from which the characteristics of

(, ) i j ελλ may be determined for a wide variety of aerosol

conditions.

What have we learned in the past two years that is

relevant to the measurement of (, ) i j ελλ over the oceans?

One important finding is that the FRSR can measure ( ) a τ λ to

an accuracy of 0.02-0.03 on a relatively large ship (>200 feet)

in typical sea-states. Our accuracy estimates are based on

comparisons with manually operated sun photometers and atsea Langley calibrations, however, as we will demonstrate

subsequently, the uncertainty in ( ) a τ λ varies from

observation to observation and must be computed

independently for each measurement.

Theoretically, most of the variability in (, ) i j ελλ is

attributable to ( ) a τ λ and caused by different aerosol

loadings, although significant variability can also be present

due to the wavelength dependence of 0 (, , )i p θθ λ . A critical

question is therefore: can the FRSR measure ( ) a τ λ with the

accuracy required to meet the specification for

∆ ≤ ε ( 0.08) required for the 5% limit on water-leaving

radiance? Error analysis suggests that the FRSR meets this

criterion under many conditions, although difficulty is

experienced in relatively clean air masses. We are thus

encouraged to proceed, keeping in mind that the variability

due to 0 (, , )i p θθ λ remains unknown due to the lack of a

technique from which to measure it continuously over water.

Frsr Data Analysis Database Summary

Seminal to the analysis of data from the shipboard FRSRs

is a proper understanding of the uncertainty (error

propagation) in measurements. This uncertainty is manifested

in three forms: pure uncertainty due to instrument noise and

design; uncertainty in the shadow-band technique (particularly

in shadow identification in cloudy conditions); and uncertainty

in the application of Beer’s law to determine ( ) a τ λ when the

instrument being employed for this purpose generates noise.

The first two sources of uncertainty can be effectively

quantified and minimized, while the last varies widely from

measurement-to-measurement and must be computed for each

data point. We have recently written a NASA TM concerning

the analysis of uncertainty and repeat the variance equations

for ( ) a τ λ and α , the Angstrom exponent for a brief

discussion. These variance equations are given by

0

2 2 2

2

0

1 N I I

mI I N

λ λ

λ τ

λ λ

σ σ

σ

       = +              

 (19.3)

1 2

1/2 2 2

2

1 2

λ λ τ τ

α

λ λ

σ σ

σ α

τ τ

     = +          

 (19.4)
SIMBIOS Project Annual Report

134

wherem is the air mass and Iλ is the direct-normal

irradiance. The subscripts 0 and N indicate the extraterrestrial value and the measured value, respectively, and the

subscripts 1 and 2 indicate the two wavelengths used in the

calculation of the Angstrom exponent. These variance

equations provide a means to evaluate 2 σ for each individual

measurement, thereby permitting a running assessment of the

quality of the aerosol measurements. The variance equations

also permit the data to be filtered according to the uncertainty

in ( ) a τ λ , α , or both variables. It should also be noted that

the uncertainty in the Angstrom exponent is inversely

proportional to a τ , so constraints on 2 σ α will naturally favor

observations with large optical thickness.

19.4 RESEARCH RESULTS

There are currently seven PRPs with their resident

FRSRs. They are typically deployed on medium to large ships

making lengthy voyages, preferably months, or on any ship

that is traveling to a unique part of the world’s oceans. Data

are collected continuously from the instrument using a laptop

computer, recorded on CD or disk, and shipped to BNL for

analysis. Calibration of the FRSRs is done at regular intervals

(six months to one year) by transporting the instruments to

Mauna Loa and deploying them for a period of two weeks to

one month. Spot calibrations are performed during the cruise

when meteorological conditions permit, but these calibrations

are used primarily to diagnose drift in the laminated

interference filters for the various channels. The procedure

used to compute the spectral direct-normal irradiance with the

FRSR is discussed in Reynolds et al. (1999) and not repeated

here.

We have performed extensive comparisons of FRSR data

with coincident data collected with other sun photometers (see

Reynolds et al., 1999). These comparisons show that the

shipboard FRSRs can measure ( ) a τ λ with accuracy that is at

least as good as hand-held and platform-stabilized narrowbeam sun photometers. It is also noteworthy that the FRSR

measures the diffuse component of irradiance, which can be

used to evaluate the sky state at the time of the observation.

Results for six deployments of the FRSR summarized in

this report are presented in Table 19.1. These cruises cover

portions of all three oceans and represent a wide range of

aerosol conditions. The six cruises include a total of 42,577

two-minute observations (1419 hours/~177 separate days)

from the FRSRs collected in sky conditions that allow a

shadow to be identified when the solar disk occulting arm

passes over the detectors. From these observations, 15,275

(509 hours, or 35% of total observations) passed a cloud filter

that selects measurements that are not influenced by clouds

between the solar disk and the FRSR detectors. The

uncertainty of each individual cloud-filtered measurement of

( ) a τ λ and α [i.e. 2 στ and 2 σ α ] was computed and

observations were accepted if 2

( ) 0.03 σ λ τ ≤ and

2

( ) 0.3 σ λ α ≤ , a stringent requirement. Only 682

observations (56 hours, or 4.4% of the cloud filtered

observations) passed the uncertainty filter.

Data that passed the uncertainty bounds are presented in

Figure 19.1, which shows τ (865) plotted against

α λ (865, ) for all six cruises. In this figure, α λ (865, ) was

computed using only the data from the two wavelengths,

rather than as a fit to all the data. The latter method provides

less detail. Figure 19.1 shows that the data from various parts

of the oceans contain complex relationships in the τ (865),

α λ (865, ) phase space. The data points in the upper right

hand portion of the phase spaces (separated with a dashed

line) were collected in regions of biomass burning off the

coast of Africa and over the Indian Ocean, as revealed by

chemical analysis of the aerosol.

Excluding these regions of biomass burning, the τ (865),

α(865,415) and τ (865), α(865,610) phase spaces

show less differentiation between various regions of the

oceans than theτ (865), α(865,500) and τ (865),

α(865,660) phase spaces. Theτ (865), α(865,660)

phase space shows the sharpest differentiation between

various regions of the oceans and shows large variability

between the five sources of data from the Pacific Ocean

[α(865,500) varies from –2 to 2].

Table 19.1. FRSR deployments

Platform Experiment Location Other AOT data Duration

R/V Ron Brown Aerosols99 Atlantic Ocean Microtops, SIMBAD, lidar 1 month

INDOEX Indian Ocean Microtops,SIMBAD,PREDE, lidar 1 months

R/V Ron Brown Nauru99 Tropical W. Pacific Microtops, lidar 1 months

R/V Mirai Nauru99 Tropical W. Pacific Microtops 1.5 months

Island of Nauru Nauru99 Tropical W. Pacific Full complement 2 months

R/V Melville SIMBIOS Gulf of California Full complement 1 month

R/V Polar Star U. Miami Australia-Mexico-US 2 months
SIMBIOS Project Annual Report

135

0 0.05 0.1 0.15 0.2 0.25 −1.5

−1

−0.5

0

0.5

1

1.5

2

AOT(870 nm)

Angstrom exponent (870, 415)

0 0.05 0.1 0.15 0.2 0.25 −1.5

−1

−0.5

0

0.5

1

1.5

2

AOT(870 nm)

Angstrom exponent (870,500)

0 0.05 0.1 0.15 0.2 0.25 −1.5

−1

−0.5

0

0.5

1

1.5

2

AOT(870 nm)

Angstrom exponent (870,610)

0 0.05 0.1 0.15 0.2 0.25 −1

−0.5

0

0.5

1

1.5

2

2.5

AOT(870 nm)

Angstrom exponent (870,660)

Figure 19.1. Plots of τ (865) versus α λ (865, ) for the six cruises. Atlantic and Indian Ocean transects

(Aerosols99/INDOEX) are shown as circles and a Pacific transect (Polar Star) as plus symbols. Data from the Tropical

Western Pacific (Nauru99) are shown as squares, triangles, and diamonds, and data from the Gulf of California and

California coastal waters are indicated with stars. The dashed line distinguishes aerosol associated with biomass burning

(upper right corner of phase space). Note the change in the Angstrom scale for α(870,660) .
SIMBIOS Project Annual Report

136

400 450 500 550 600 650 700 750 800 850 900

0

0.05

0.1

0.15

0.2

0.25

Wavelength (nm)

AOT

Statistics for All Cruises 

Figure 19.2. Composite of aerosol optical thickness versus wavelength for all six cruises that pass the cloud and uncertainty

filters. Means are indicated with circles and the standard deviations as error bars.

A conspicuous feature of the phase plots is the presence

of negative and near-zero two-wavelength Angstrom

exponents in the data from every cruise, a feature that has

been linked through chemical analysis to the occurrence of

sea-salt aerosol. The singular exception to this rule is

α(865,660) in the Atlantic and Indian Oceans where almost

all Angstrom exponents are positive in this wavelength range,

regardless of the type of aerosol that is present (i.e. sea-salt or

biomass burning). We view this observation with caution,

however, since filter degradation at 660 nm is a known

problem for the type of detectors used in the FRSR.

Moreover, coincident data from hand-held sun photometers do

not validate this conclusion.

Statistics for the aerosol optical properties summarized in

this report are presented in Figures 19.2 and 19.3. Figure 19.2

shows the mean and standard deviation as a function of

wavelength for all data passing the stringent uncertainty filter

outlined in the previous paragraph. It should be emphasized

that the standard deviations represent the variation in the

aerosol optical thickness values over the ocean rather than

instrument uncertainty, which has been minimized by the

filtering technique. The large standard deviations, particularly

in the visible wavelengths, are a fundamental problem when

correcting satellite ocean color for atmospheric aerosol

scattering. We assume that the lower bound on the standard

deviations roughly represents typical aerosol conditions in

clean air masses far removed from continental sources, while

the upper bounds represent conditions in polluted air. 

The mean and standard deviation of the data passing the

uncertainty filter sorted by ocean are presented in Figure 3(ac). The experiments during which these ocean-segregated data

were collected are Aerosols99 (Atlantic Ocean), INDOEX

(Indian Ocean), and Polar Star Cruise (Pacific Ocean). Cruise

trajectories suggest that the data collected over the Atlantic

Ocean should have less continental influence, relative to the

Pacific and Indian Ocean Cruises, an idea reinforced by the

generally lower aerosol optical thickness observed in the
SIMBIOS Project Annual Report

137

400 450 500 550 600 650 700 750 800 850 900

0

0.1

0.2

400 450 500 550 600 650 700 750 800 850 900

0

0.1

0.2

400 450 500 550 600 650 700 750 800 850 900

0

0.1

0.2

400 450 500 550 600 650 700 750 800 850 900

0

0.1

0.2

400 450 500 550 600 650 700 750 800 850 900

0

0.1

0.2

Wavelength (nm)

AOT

Atlantic Ocean

Indian Ocean 

Pacific Ocean 

Tropical Western Pacific Ocean 

California Coastal Waters and Gulf of California 

Figure 19.3. Means (circles) and standard deviations (error bars) for all data collected in the region specified in the upper right

hand corner of the plot.

visible wavelengths. The Angstrom exponents for these

Atlantic data are nearly zero, a characteristic that we will

demonstrate for other parts of the ocean devoid of continental

influence. Data from the Indian Ocean suggest aerosol optical

thickness values that are nearly double the values found over

the Pacific and Atlantic. The Indian Ocean data also exhibit

extreme variability.

Additional data from sub-regions of the Pacific Ocean

collected during regional cruises and an island deployment of

the FRSR are shown in Figure 19.3 (d-f). The sub-regions are

the Tropical Western Pacific (Nauru99, three platforms) and

the California coast and Gulf of California (SIMBIOS

Melville Cruise). Data from the Tropical Western Pacific

indicate low optical thickness, little variability, and Angstrom

exponents near zero or negative for all channels. As discussed

above, data from the Atlantic Ocean also indicate Angstrom

exponents near zero, although the optical thickness is

somewhat larger than in the Tropical Western Pacific.

Composite data from a Pacific transect (the Polar Star cruise)

are similar in optical structure to the data taken from the

California Coast (the Melville cruise), but also contain lower

bounds that include the optical structure of the Tropical

Western Pacific. We therefore conclude that the data from the

Polar Star Cruise that transects the Pacific Ocean appear to

faithfully represent the variety of conditions that may be

observed.

19.5 CONLUSIONS

The analysis of the data from the six cruises presented

here is ongoing and data from additional deployments are

being processed. Nonetheless, some conclusions can be

drawn from this analysis. First, aerosol optical thickness data

subject to stringent uncertainty bounds suggest that the

Angstrom exponent in clean oceanic air masses far removed
SIMBIOS Project Annual Report

138

from continental sources is nearly zero. This result suggests

that there is no mechanism present in the marine boundary

layer that can produce sea-salt particles as small as the

particles contained in polluted air masses. This is not

surprising, since sea-salt is deliquescent and humidity is high.

It is interesting to note that aerosol data collected over the

Atlantic Ocean have systematically higher aerosol optical

thickness than their counterparts over the Tropical Western

Pacific. This observation may be explained by the smaller

area of the Atlantic Ocean, relative to the Pacific, and Trade

Winds in the Tropical Western Pacific. Trade winds may

permit long over-ocean trajectories for air masses arriving in

the Tropical Western Pacific. While dry deposition of

continental aerosol may naturally cleanse the air mass, the

presence of sporadic tropical convection may act as an

additional source of aerosol deposition.

The air mass observed over the Indian Ocean was highly

polluted. On the downside, this observation suggests that the

atmospheric correction for ocean color measurements will be

large over this region. On the upside, this may be the only

region that truly fits the requirements for the current

atmospheric correction algorithm. We make this contention

on the basis of Figure 19.3 because it suggests that the

Angstrom exponent in the near infrared does not differ

significantly from region to region, despite significant

differences in aerosol optical impacts in the visible. We

surmise that the current atmospheric correction algorithm

should perform well in highly polluted air masses, which is the

good news. In contrast, we suggest that the current

atmospheric correction algorithm is destined to fail in

relatively clean air masses, or polluted air masses that possess

the characteristics shown in Figure 3c. Because of this

optical structure, which appears to be present over relatively

large areas of ocean, we endorse the development of a new

atmospheric correction scheme based on climatology, perhaps

supplemented by aerosol transport models. Another approach

may be to incorporate additional data to help select an

appropriate aerosol model, since α(870,765)shows little

information leading to an accurate specification of

τ (870, 765) wavelengths < .

It can be argued that the correction factor will be small in

clean air masses, so the fact that the current algorithm may not

perform well under these conditions may be considered

tolerable. To the contrary, we argue that the area of the

oceans covered by relatively clean air is likely to be much

larger than that covered by polluted air, so slight errors in the

correction scheme are greatly compounded in the context of

global estimates.

REFERENCES

Gordon, H.R. 1978: Removal of atmospheric effects from

satellite imagery of the oceans. Applied Optics, 17, 1631-

1636.

Gordon, H.R., D.K. Clark, J.L. Mueller, and W.A. Hovis

1980: Phytoplankton pigments derived from the Nimbus7 CZCS: initial comparisons with surface measurements.

Science, 210, 63-66.

Gordon, H.R., and D.K. Clark 1981: Clear water radiances for

atmospheric correction of coastal zone color scanner

imagery, Applied Optics, 20, 4175-4180.

Gordon, H.R. and M.Wang 1994: Retrieval of water-leaving

radiance and aerosol optical thickness over the oceans

with SeaWiFS: a preliminary algorithm. Applied Optics,

33, 443-452.

Reynolds, M.R., M.A. Miller, and M.J. Bartholomew, 1999: A

fast-rotating, spectral shadowband radiometer for marine

applications, J. Atmos. Ocean. Tech., (in press).

This research was supported by the

SIMBIOS NASA interagency agreement # 97888

PEER REVIEWED PUBLICATION

Benkovitz, C.M., M.A. Miller, S.E. Schwartz, and O. Kwon,

2000: The influence of atmospheric dynamics on the

distribution and loading of SO2 and sulfate over the North

Atlantic and Europe, G3 (electronic publication),

submitted.

Frouin, R., B. Holben, M.A. Miller, C. Pietras, E. Ainsworth,

J. Porter, and K.Voss, 2000; Sun photometer and sky

radiance measurements and data analysis protocols.

Chapter 11; Ocean optics protocols for satellite ocean

color sensor validation, revision 2; NASA Tech. Memo.

2000-20996, edited by G.S. Gargion and J.L. Mueller.

Pietras, C.M., M.A. Miller, E.Ainsworth, R.Frouin, B.Holben,

and K.Voss, 2000: Calibration of sun photometers and
SIMBIOS Project Annual Report

139

sky radiance sensors; in Chapter 5; Ocean optics protocols

for satellite ocean color sensor validation, revision 2;

NASA Tech. Memo. 2000-20996, edited by G.S. Gargion

and J.L. Mueller.

Porter, J.N., M.A. Miller, C. Motell, and C.Pietras, 2000: Use

of hand-held sun photometers for measurements of

aerosol optical thickness at sea. Submitted to J. Atmos.

Ocean. Tech.(in press).

Reynolds, R.M., M.A. Miller, and M.J. Bartholomew, 2000:

Design, operation, and calibration of a shipboard fastrotating shadowband spectral radiometer. Submitted to J.

Atmos. Ocean. Tech.(in press)

Reynolds, R.M., M.A. Miller, and M.J. Bartholomew, 2000: A

fast-rotating, spectral shadowband radiometer for marine

applications, J. Atmos. Ocean. Tech., (in press)

Voss, K.J., E.J. Welton, P.K. Quinn, R. Frouin, M.A. Miller,

and R.M. Reynolds, 2000: Aerosol optical depth

measurements during the Aerosols99 experiment, J.

Geophys. Research, (accepted).

Quinn, P.K., D.J. Coffman, T.S. Bates, T.L. Miller, J.E.

Johnson, E.J. Welton, C. Neusüss, M.A.Miller, and

Sheridan, P 2000: Aerosol Optical Properties during

INDOEX 1999: Means,Variability, and Controlling

Factors, J. Geophys. Research, (submitted).

PRESENTATIONS

Benkovitz, C.M., M.A. Miller, S.E. Schwartz , and R.C.

Easter, 2000: Effects of Sulfur Emissions from

Popocatepelt Volcano on the Central U.S. in June 1997;

AGU Fall Meeting

Miller, M.A., R. M. Reynolds, and M.J. Bartholomew, 1999:

Aerosol optical thickness and diffuse irradiance

measurements during INDOEX, AGU Meeting.

Reynolds, R.M. and M.A. Miller, 1999: Instrument

demonstration at the 9th Annual ARM Science Team

Meeting, San Antonio, Texas.
SIMBIOS Project Annual Report

140

Chapter 20

Measurements of Aerosol, Ocean and Sky Properties at the HOT

Site in the Central Pacific.

John N. Porter and Ricardo Letelier

Hawaii Institute of Geophysics and Planetology, University of Hawaii, Honolulu, Hawaii

20.1 INTRODUCTION

Monthly cruises have been made to the HOT site (~100

km north of Oahu) since October 1988. The goal of these

cruises is to make hydrography, chemistry, and biology

observations (PI: Dave Karl and Roger Lucas). Measurements

are typically made at a near coastal station (Kahe) on the first

day to test the equipment and to obtain coastal shallow water

(~1500m) observations. The second and third days are spent at

the HOT site. On the morning of the fourth day, measurements

are made at the HOT site and noontime measurements are

made at the Hale-Aloha station near the mooring before

returning to port by early the next morning. The locations of

the three stations are:

• Kahe (21.34o

 N, 158.27o W)

• HOT (22.75o

 N, 158.0oW)

• Hale-ALOHA buoy (22.43o

N, 158.0oW).

The routine HOT measurements are available during the

summer following the year of the observations. The 1998

measurements will therefore become available during the

summer of 1999. The data sets can be obtained at the National

Oceanographic Data Center (NODC) or from the HOT web

site http://kahana.soest.hawaii.edu/hot/hot_jgofs.html.

20.2 RESARCH ACTIVITIES

Microtops Sun Photometer Measurements

Beginning with the HOT89 cruise, aerosol optical depth

measurements were made with two Microtops sun

photometers. Calibration, proper measurement protocols and

data analysis of the Microtops measurements were our initial

concerns. The difficulty in making measurements at sea from

a moving platform raised serious questions about the quality

of the data. It was found that under rough conditions, it was

difficult to point at the sun resulting in many measurements

being biased towards larger optical depths. In order to

minimize this bias, a large number of sun photometer

measurements (~25) should be taken in a short period of time

and the higher values should be discarded in post processing.

Under rough ocean conditions, it is also best to shorten the

Microtops sun photometer-sampling period (less than 5 sec.)

and save only a single value (no averaging). It was also is

found that the Microtops should be turned off frequently to

correct for zero drift caused by temperature effects. Further

discussion of the use of Microtops sun photometers on ships is

given in Porter et al., 2000. A similar version of this approach

is now being used by the SIMBIOS group efforts with their

large collection of Microtops sun photometer data.

Calibration for our Microtops were maintained by routine

Langley plot calibrations at the Mauna Loa Observatory. This

was found to be more accurate than the cross calibration

approach used by the SIMBIOS program (Porter at al., 2000).

Although this involves additional effort, it also allowed us to

test the Microtops under different conditions. It was also

found that, even at a clean site like the Mauna Loa

observatory, the more calibrations performed the better and

outliers must be removed.

A time series of aerosol optical depth measurements made

on the HOT cruises are shown in Figure 23.1 for 380 nm. An

annual cycle is clearly present with a maximum during

springtime, which is consistent with Asian transport. At longer

wavelengths (up to 1020 nm) the annual cycle is less evident

suggesting the springtime Asian transport during 1998 brought

significant accumulation mode aerosol to the Central Pacific,

which is most likely due to anthropogenic pollution. A similar

feature is seen in the SeaWifs images processed for the HOT

region for a portion of the same time period. This is shown in

figure 23.2 which shows the ratio of aerosol scattered light at

two wavelengths (780 and 860 nm) (eps_78). Larger ratio

values correspond to smaller aerosol and smaller values to

larger aerosol. Similar to the aerosol optical depth

measurements, the eps_78 suggest a seasonal cycle of smaller

aerosol over the HOT site during the springtime.

Aerosol optical depth measurements were made at 380,

440, 500, 675, 870 and 1020 nm, column integrated water

vapor and ozone concentrations were also derived. These

measurements have been submitted to the SeaBASS archive.
SIMBIOS Project Annual Report

141

Figure 20.1. Left panel shows time series of aerosol optical depth (380 nm) at the HOT site. Right panel shows eps_78 derived

from SeaWifs for the HOT site.

Figure 20.2. Comparison between Seawifs and ship derived aerosol optical depths (870 nm).

Figure 20.3. The figure shows the spectral dependence of the aerosol optical measured on January 10-12, 1998 at the HOT site.

Some comparisons between ship and satellite optical

depths are shown in figure 20.2. The best comparisons are

shown as blocks and the x values are cases where ship and

satellite were close but not exactly the same. In general the

agreement is within 0.03 but several outliers are present.

These outliers usually typically occur with flat spectral optical

depths, which may be dust. We have investigated the

possibility of deriving aerosol size distributions from the
SIMBIOS Project Annual Report

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spectral information in the aerosol optical depths (Lienert et

al., 2000; Porter et al., 2000). As an example of this inversion

process, Fig. 20.3 shows a set of aerosol optical depth

measurements made on HOT 89 cruise. On day 10 the aerosol

was typical marine aerosol with a flat spectral shape. By day

12 it had changes to volcanic aerosol with a strong spectral

shape, which is typical when the accumulation mode dominate

as in city pollution. These two days illustrate polluted versus

marine conditions. Figures 23.4 and 20.5 show the inversion

results from the optical depths collected on day 10 and 12. It

can be seen that for day 10 the various aerosol size

distributions which were derived were approximately the same

but that for day 12 they are all over the place. This means that

when coarse mode aerosol are present the aerosol optical

depths up to 1 um are not sufficient to constrain the

measurements. We are now working on a more empirical

approach (lookup table) to estimate the coarse mode based on

realistic aerosol models (Porter and Clarke, 1997).

In-Water Optics Measurements

As part of this SIMBIOS effort, routine optical

measurements have been made at each of the HOT stations.

Measurements are made simultaneously with a PRR (Profiling

Reflectance Radiometer) and a TSRB (Tethered Spectral

Radiometer Buoy). These instruments provide measurements

in the visible wavelength range of downwelling irradiance and

upwelling radiance at the sea surface and at depth. These

measurements have been made since the HOT89 cruise

(January 1998) by Karl et al. and are processed by R. Letelier

and J. Bartlett. Deployment and calibration procedures follow

the recommended protocols for SeaWiFS calibration and

validation. Immediately following each monthly HOT cruise,

the PRR data are processed and submitted to SeaBASS. The

processed PRR and TSRB data are also available online for

use by the scientific community

http://picasso.oce.orst.edu/ORSOO. This web site also

provides details of the measurement locations,

instrumentation, and deployment procedures. The optical data

resulting from this ongoing effort will aid in the interpretation

of the temporal and spatial variability of the constituents of

Hawaiian waters.

Sky Radiometer

We have recently begun using a manual radiometer to

measure sky radiance from ship and aircraft platforms. The

goal is to collect spectral radiance measurements from

scattering angles of 3-25°. The spectral measurements are then

used in the inversion process described above to better restrict

the aerosol possibilities. One example of these measurements

is shown in Fig. 20.6.

Measurements of the Aerosol Scattering and Absorbing

Coefficients and the Aerosol Phase Function

During 1998 a prototype polar nephelometer was built to

measure the aerosol phase function (Porter et al., Hilo Ocean

Optics meeting in 1998). Two new improved versions have

been built. Measurements are made at three wavelengths

(1064, 532, 355 nm) and include the aerosol scattering

coefficient and aerosol phase function from ~3 to 177°. An

example from our ground based version is shown in Figure

23.7 for marine aerosol.

As part of this effort a soot absorption photometer was

also acquired. Measurements on several HOT cruises during

the spring time had absorption values of 1x10-5 m-1 which is

surprisingly high and further suggest Asian transport to the

Central Pacific. During selected future HOT cruises we will be

making measurements of the aerosol scattering and absorption

coefficients. This should help to better understand the aerosol

properties of the Central Pacific.

Marine Shadowband Radiometer

During the past three years we have been working on a

custom marine shadowband radiometer which will save

aerosol optical depths and downwelling irradiance at many

wavelengths. This system uses a gimbaled cosine response

detector and a rotating shadowband arm which shadows the

detector. A temperature controlled CVI Laser spectrometer is

used to collect the light which is saved on a portable

computer. The system has been tested on several cruises and

the gimbal works well. A new improved cosine response

collector has been built and is now being tested. Although the

system is not yet complete, we anticipate it will be soon and

will be used on future cruises.

Modeling Efforts

We have several topics we are looking at using modeling

efforts. These include algorithms to derive information on the

aerosol type from sun photometer measurements, studies of

the surface reflection problem, and satellite retrievals of

aerosol type and the wavelength dependence of aerosol

radiance.

In order to study these effects we have developed an

atmospheric Monte Carlo model. This code has been

compared successfully with the Nakajima RTRN algorithm for

a Lambertian surface. A Cox and Munk model modified for

whitecaps will be used for the final calculations.
SIMBIOS Project Annual Report

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Figure 20.4. Left panel shows the aerosol distributions derived from sun photometer measurements made on day 12 in Fig. 11.

Figure 20.5. Right panel shows the aerosol distributions derived from sun photometer measurements made of day 10 in Fig. 11.

Figure 20.6. Spectral sky radiance measurements made on a HOT cruise from a manual sky radiometer. Measurements shown

here are for 3.1, 5.3, 9.7, and 25.6° from the sun.

0.01 0.1 1 10

0.000

0.002

0.004

0.006

0.008

0.010

Unimodal Optical thickness Inversion

fit <= 0.01

dA/dloge(diam) x106 m-1

diameter, microns

0.01 0.1 1 10

0.000

0.002

0.004

0.006

0.008

0.010

Bimodal Optical thickness inversion

fit <= 0.01

dA/dloge(diam) x106 m-1

diameter, microns
SIMBIOS Project Annual Report

144

Figure 20.7 Aerosol phase function measurements made at Bellows Beach with our ground based polar nephelometer. Here

five separate scans are layed on top of each other showing the measurements are reproducible. During the measurements

marine sea spray was dominant but pollution from Asia was also present.

REFERENCES

Lienert, B.R., J.N. Porter, and S.K. Sharma, 2000: Repetitive

Genetic Inversion of Optical Extinction Data, Applied

Optics (submitted).

Porter, J., Clarke, A., 1997: An Aerosol Size Distribution

Model Based on In-Situ Measurements. J. Geophy. Res.,

102, 6035-6045.

Porter, John N., Tom F. Cooney and Craig Motell, 1998:

Coastal aerosol phase function measurements with a

custom polar nephelometer. Ocean Optics XIV

Conference, Kona, Hawaii.

Porter, J.N., Lienert, B., S.K. Sharma, 2000: Using the

Horizontal and Slant Lidar Calibration Methods To

Obtain Aerosol Scattering Coefficients From A Coastal

Lidar In Hawaii. Journal of Atmospheric and Oceanic

Technology (in press).

Porter, J, Miller, M., Pietras, C., 2000: Use of the MicroTops

Sunphotometer On Ships. Journal of Atmospheric and

Oceanic Technology (accepted).

Porter, J. N., A.D. Clarke, and B. R. Lienert, 2000:

Aircraft/Surface Derived Aerosol Optical Properties Near

Hawaii For Satellite Validation, SPIE Remote Sensing of

the Atmosphere, Environment and Space, Sendai, Japan.
SIMBIOS Project Annual Report

145

This research was supported by the

SIMBIOS NASA interagency agreement # 97136

PEER REVIEWED PUBLICATION

Porter, J., Clarke, A., 1997: An Aerosol Size Distribution

Model Based on In-Situ Measurements. J. Geophy. Res.,

102, 6035-6045.

Porter, J.N., Lienert, B., S.K. Sharma, 2000: Using the

Horizontal and Slant Lidar Calibration Methods To

Obtain Aerosol Scattering Coefficients From A Coastal

Lidar In Hawaii. Journal of Atmospheric and Oceanic

Technology (in press).

Porter, J, Miller, M., Pietras, C., 2000: Use of the MicroTops

Sunphotometer On Ships. Journal of Atmospheric and

Oceanic Technology (accepted).

Lienert, B.R., J.N. Porter, and S.K. Sharma, 2000: Repetitive

Genetic Inversion of Optical Extinction Data, Applied

Optics (submitted).

OTHER PUBLICATIONS

Porter, John N., Tom F. Cooney and Craig Motell, 1998:

Coastal aerosol phase function measurements with a

custom polar nephelometer. Ocean Optics XIV

Conference, Kona, Hawaii.

Porter, J. N., A.D. Clarke, and B. R. Lienert, 2000:

Aircraft/Surface Derived Aerosol Optical Properties Near

Hawaii For Satellite Validation, SPIE Remote Sensing of

the Atmosphere, Environment and Space, Sendai, Japan.

.
SIMBIOS Project Annual Report

146

Chapter 21

Assessment of the Contribution of the Atmosphere to

Uncertainties in Normalized Water-Leaving Radiance: A

Combined Modeling and Data Analysis Approach.

Knut Stamnes, Wei Li and Bingquan Chen

 Department of Physics and Engineering Physics, Stevens Institute of Technology, New Jersey

21.1 INTRODUCTION

Our research for the SIMBIOS program has been focused

on modeling and simulation studies as well as the

development of atmospheric correction algorithms. Based on

our original proposal and discussions at the SIMBIOS team

meetings and workshops, the objectives of our research can be

summarized as follows: (i) Use our radiative transfer model

for the coupled atmosphere-ocean system to simulate the

radiation field at arbitrary levels and in any desired direction

in the atmosphere and ocean so as to provide a firm

connection between the signal received by the satellite sensor

and by a sensor looking down into the water column just

above the surface and just below it. (ii) Use the simulations to

quantify the influence of atmospheric aerosols on the waterleaving radiance, and to quantify the error in the water-leaving

radiance as a function of uncertainties in the aerosol optical

properties, mass loading and vertical extent. (iii) Use the

model simulations in conjunction with validation

measurements taken by other SIMBIOS investigators (for

satellite overpasses) to assess our understanding of the

radiative transfer process in the coupled atmosphere-ocean

column, and to examine the extent to which the model

provides a realistic prediction of simultaneously measured in

situ water-leaving radiance and the radiance received by the

satellite sensor. (iv) Modify and improve an existing

atmospheric correction algorithm, based on the work above, as

needed by constructing new look-up tables that include

scattering by ocean particles. (v) Carry out approaches to

developing an atmospheric correction algorithm for ocean

color imagery with strongly absorbing aerosols.

21. 2 RESEARCH ACTIVITIES

We have carried out studies aimed at examining the

accuracy of methods employed for computing aerosol optical

properties. Accurate aerosol optical properties are critically

important for atmospheric correction as well as for

development of algorithms to retrieve aerosol optical

properties and chlorophyll concentration.

Algorithm Development

We have developed a new algorithm for simultaneous

retrieval of the chlorophyll concentration [C] and the aerosol

optical properties [τa(865) and model]. We have used synthetic

data to test the basic soundness of our algorithm. Work on

processing of satellite data and comparing our results with

those from Gordon and Wang's algorithm and Siegel's

algorithm is in progress.

Collaborations

Collaborations with other PIs in the SIMBIOS Program

and collaborations with other investigators in ocean optics and

atmospheric correction: (a) We have had close collaborations

with Dr. S.-C. Tsay at NASA GSFC on this study. (b) We

have been collaborating with Drs. J.J. Stamnes and O. Frette at

University of Bergen, Norway on the problems of atmospheric

correction as well as bio-optical models appropriate for use in

open ocean and coastal waters. (c) We have been collaborating

with Dr. Menghua Wang at SIMBIOS Project Office on

resolving the aerosol issue.

21.3 RESEARCH RESULTS

We have compared the following two methods in

computing aerosol optical properties: (i) Method 1 (Shettle

and Fenn, 1979): (a) An effective refractive index is

generated for a multi-component aerosol population: i.e. the

multi-component aerosol model is reduced to a single

component with an effective refractive index. (b) The optical

properties of the aerosols are computed based on this singlecomponent aerosol model with its effective refractive index.

(ii) Method 2 (Tsay and Stephens, 1990): (a) For a multicomponent aerosol, the optical properties of each aerosol

component are computed separately. (b) The optical properties

of the aerosols are computed as the concentration-weightedaverage of the optical properties of each aerosol

component. Comparisons between methods 1 and 2 for
SIMBIOS Project Annual Report

147

computing TOA reflectances indicate that the difference is

more than 10%, which is larger than the total contribution by

the ocean. Thus, this error or uncertainty is significant and

must be corrected.

 Removing the aerosol contribution from the measured

TOA radiance is the most important issue in atmospheric

correction. Accurate removal of the aerosol contribution

requires: (i) access to accurate aerosol optical properties; (ii) a

critical re-examination of the adequacy of current aerosol

models. Accurate computation of aerosol optical properties is

critically important for atmospheric correction and ocean

chlorophyll retrieval. Further studies are required to establish

which method (Shettle and Fenn, or Tsay and Stephens, or

other) is adequate for computing aerosol optical properties.

More data on aerosol optical properties and their variability

over the world's oceans are needed for testing and validation

of theoretical approaches.

Algorithm Development

We have developed a new algorithm for simultaneous

retrieval of the chlorophyll concentration and the aerosol

optical properties based on a radiative transfer model for the

coupled atmosphere-ocean system that includes rigorous

multiple scattering computations. The algorithm is described

in the figure 24.1: “Flowchart: Simultaneous retrieval of

aerosol optical properties and chlorophyll concentration”. Our

new approach does not employ the black-pixel assumption. It

relies on channels at λ = 490, 555, 765, and 865 nm, and on an

iterative method for simultaneous retrieval of the chlorophyll

concentration [C] and the aerosol optical properties [τa(865)

and model]. This algorithm can be applied to non-absorbing,

weakly-absorbing as well as strongly-absorbing aerosols.

ACKNOWLEDGMENTS 

This work was supported by the National Aeronautics and

Space Administration (NASA) under the contract NAS5-

97138.

This research was supported by the

 SIMBIOS NASA contract # 97138

PEER REVIEWED PUBLICATIONS

Bingquan Chen, Knut Stamnes, Banghua Yan, Oyvind Frette,

and Jakob J. Stamnes, 1998: Water-leaving radiance in

the NIR spectral region and its effects on atmospheric

correction of ocean color imagery. Journal of Advanced

Marine Science and Technology Society, Vol. 4, No. 2,

329-338.

PRESENTATIONS

Yan, Banghua, Knut Stamnes, and Bingquan Chen, 1999: A

Generalized Algorithm for Atmospheric Correction of the

Ocean Color Imagery: Feasibility Simulation. AGU 1999

Spring Meeting in Boston.

Knut Stamnes, Wei Li, and Bingquan Chen, 2000: A simple

inversion algorithm for simultaneous retrieval of aerosol

optical properties and chlorophyll concentration.

Workshop on Hyperspectral Imaging held at University of

Puerto Rico, Mayaguez, September 8-9, 2000.

Knut Stamnes, Banghua Yan, Wei Li, Bingquan Chen, Jakob

J. Stamnes, and Si-Chee Tsay, 2000: Atmospheric

correction for strongly absorbing aerosols. Ocean Optics

XV, Monaco, October 16-20, 2000.
SIMBIOS Project Annual Report

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SIMBIOS Project Annual Report

149

Chapter 22

SOURCES OF VARIABILITY IN CHLOROPHYLL ANALYSIS

BY FLUOROMETRY AND BY HIGH PERFORMANCE

LIQUID CHROMATOGRAPHY

Laurie VanHeukelem, Crystal S. Thomas and Patricia M. Glibert

Horn Point Laboratory, University of Maryland Center for Environmental Science, Cambridge, Maryland

22.1 INTRODUCTION

The need for accurate determination of chlorophyll a (chl

a) is of interest for numerous reasons. From the need for

ground-truth data for remote sensing to pigment detection for

laboratory experimentation, it is essential to know the

accuracy of the analyses and the factors potentially

contributing to variability and error. Numerous methods and

instrument techniques are currently employed in the analyses

of chl a. These methods range from spectrophotometric

quantification, to fluorometric analysis and determination by

high performance liquid chromatography (HPLC; Parsons et

al., 1984; Clesceri et al. 1998; Jeffrey et al. 1999). Even

within the application of HPLC techniques, methods vary (e.g.

Goericke and Repeta 1993, Mantoura and Llewellyn 1983,

Wright et al. 1991, VanHeukelem and Thomas in press). Here

we provide the results of a comparison among methods and

provide some guidance for improving the accuracy of these

analyses.

These results are based on a round-robin conducted

among numerous investigators, including several in the

SIMBIOS and HyCODE Programs. Our purpose here is not to

present the full results of the laboratory intercalibration; those

results will be presented elsewhere. Rather, here we highlight

some of the major factors that may contribute to the variability

observed. Specifically, we aim to assess the comparability of

chl a analyses performed by fluorometry and HPLC, and we

identify several factors in the analyses which may contribute

disproportionately to this variability.

Experimental Design

There were three aspects to this study that are analyzed

here. The first is a comparison of standards of pure chl a

analyzed by the participant laboratories using fluorometry and

HPLC. The second aspect examines the effect of the

difference between instrumental approaches when the

standards of chl a have a variable, but known, contribution of

divinyl chl a (DV chl a). Finally, analysis of chlorophyll on

laboratory-prepared and field-collected filters were compared

using varying extraction protocols or injection techniques

prior to pigment separation and detection. All instruments

were calibrated with the same set of standards prior to the

above tests.

Preparation of the Stock Solutions

The chl a stock solution was prepared by dissolving chl a

granules (Fluka 25730) in 90% acetone and initially

determining the stock concentrations by spectrophotometric

analysis (Clesceri et al. 1998). A Hitachi U-3100

spectrophotometer, with 2 nm bandwith, was employed.

Absorbances between 0.1 and 1.0 were read using an

extinction coefficient of 87.67 at 664nm. The absorbance

accuracy of this instrument was verified using National

Institute of Standards and Technology traceable neutral

density filters (Starna cells RM-N1N35N and RM-1N2N3N).

For distribution to the various participants, standards of 5

calibration levels were prepared. All dilutions were made

using Class A volumetric glassware and gas-tight glass

syringes. The accuracy and precision of the pipettes and

syringes were assessed prior to use. For distribution, solutions

were dispensed into amber glass vials with Teflon-lined screw

caps. Sufficient chl a standard was dispensed to allow each

investigator to analyze in replicate by each method.

Extraction Procedures

For the analysis of samples on filters, two approaches

were used. For one set of analyses, the same extraction

procedures were employed by all participants. Samples were

extracted in 90% actetone (5 ml added to filters for HPLC

analysis, and 10 ml to filters for fluorometric analysis). Filters

were allowed to soak for 3 to 4 hrs in the dark (-15o

C), after

which the extract was filtered through a Teflon HPLC syringe

cartridge filter (Scientific Resources Inc. 44525). Total

extraction volumes took into account the average contribution

of the water retained by the GF/F filters (145 μl for a 25 mm

GF/F filter and 500 μl for a 47 mm GF/F filter). Hence, for

the filters analyzed by fluorometry, the extraction volume of
SIMBIOS Project Annual Report

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10.145 ml was employed, while for the filters analyzed by

HPLC, either 5.145 ml or 5.500 ml was used, depending on

filter diameter. For the second set of analyses, participants

employed their own protocols for sample extraction (see

below).

Fluorometric Analyses

All participating laboratories analyzed the distributed

standards using methods based on Parsons et al. (1984). There

were differences among instruments employed, which will be

described in more detail in a future report.

HPLC Methods

 The methods used by participating laboratories varied

widely with respect to instrumentation as well as sample

preparation for HPLC analysis (Table 1). With most pigment

HPLC methods, the sample extract is mixed with buffer (or

water) prior to injection. Fully automated HPLC’s can

perform this step automatically. Vials containing sample

extract, and separate vials containing buffer (or water), are

placed in the auto-sampler compartment and an automated

sequence combines the buffer with sample immediately prior

to the analysis of each sample. Some autosamplers are

refrigerated. Sample vials may reside in an auto-sampler

compartment from 1 to 24 hr. An alternate method employs

an auto-sampler compartment and an automatic injector

programmed to inject a specific volume from each sample

vial, but the auto-injector cannot mix sample with buffer prior

to analysis. In this case, the analyst must individually

combine each sample with buffer at the beginning of a

sequence of analyses, which in turn results in samples residing

in the autosampler for variable lengths of time after being

mixed. Finally, when autosamplers are not available, manual

mixing of sample extract with buffer and the manual injection

of the mixture into the instrument is used.

 The method of separation and detection also varied among

laboratories. Investigators used HPLC instruments from either

Hewlett-Packard, Dionex, Waters, or Beckman. A summary

of the columns used and published mobile phases employed

are summarized in Table 1.

22.2 RESULTS

Comparison of Fluorometry and HPLC Analysis for a Pure

chl a Standard

The variation between the concentration as reported by

participants and the concentration of chl a, as initially

formulated and calibrated spectrophotometrically, ranged from

–0.4 to 11.3% for fluorometric analyses (Fig. 22.1A). When

the same standard was analyzed by HPLC (all methods), the

total difference between reported and formulated values

ranged from –30.6% and 26.4% (Fig.22.1A). A closer

inspection, however, of the variability associated with the

HPLC results indicates that accuracy was highly coupled with

HPLC injection mode. The fully automated mode of injection

resulted in significantly reduced variability. The differences

between reported and formulated values for this standard

analyzed on instruments with fully automated injection modes

was –0.23% to 1.7% (Fig. 22.1A).

Comparison of Fluorometry and HPLC Analysis for a Mixed

chl a Standard

Chl a-like products (chl a allomers epimers,

chlorophyllide a (chlide a) and DV chl a) respond similarly to

chl a in a fluorometer. However, because of different

separation and reporting practices in the use of HPLC, weaker

agreement would be expected when comparing results from

HPLC in which samples contain varying amounts of chl a

products. For the standard distributed that contained equal

proportions of chl a and DV chl a, the chl a concentration

reported by participants employing fluorometric techniques

differed from the formulated value (sum of chl a and DV chl

a) by –9.4% to 14.6% (Fig. 22.1B). This variation was

slightly higher than that seen with the discrete pure chl a

standard. At least one participant in this study

chromatographically separated DV chl from chl a in their

HPLC analyses, and hence reported individual values for each

chl component. Other participants did not employ separation

techniques that permitted the chromatographic separation of

chl a from DV chl a. Thus, in their analysis of the mixed

standard containing both chl a and DV chl a, only one peak

was seen and reported. The values reported by these

laboratories differed from the formulated value by –10% to

100% (Fig. 22.1B). When comparisons are limited to data

resulting only from instruments in the fully automated mode,

the error reduced to a range from –3.5 % to 16.1% (Fig.

22.1B).

There was some variation in the detector settings used by

participants in the HPLC analysis of chl a. This also resulted

in some degree of variability in the ability to accurately report

total chl a, in the presence of DV chl a and when chl a was

used as the calibrant (Fig. 22.2). For those laboratories using

a detector setting of 436 (4 nm bandwidth) or 665 (20 nm

bandwidth), the total chl a values reported varied by no more

than 5% from the formulated value. At other wavelengths or

bandwidths, the DV chl a response was much greater than the

chl a response, therefore the selection of these wavelengths

resulted in and overestimation of total chl a. A wavelength of

440 nm incurred the greatest error in the comparison here (Fig.

22.2).

Comparison of Fluorometry and HPLC Analysis for

Laboratory-prepared Filters

Sample filters, with a mean consensus HPLC value of

1025 ng chl a per filter and a simple suite of pigments, were

analyzed by all participants. These samples did not contain
SIMBIOS Project Annual Report

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chlide a, which can strongly affect fluorometer values and

cause poor agreement between fluorometer and HPLC if not

accounted for in the HPLC total chl a value. Additionally, the

same extraction protocol was used by all laboratories and the

chl a calibration among laboratories and between fluorometer

and HPLC was standardized. Here, the fluorometer values

ranged from 110 to 120% of the mean consensus HPLC value

(Fig. 22.3). The range seen with HPLC values was 73.7 to

121%, including all injection modes, but 96 to 105% if the

values from the manual mode of injection are excluded.

Effects Of Differing Extraction Procedures For FieldCollected Filters

In order to assess the effect of varying extraction

protocols on the analysis of chl a by both fluorometry and

HPLC, it was first necessary to establish the agreement that

could be achieved within one laboratory when the identical

extraction method was used. This analysis, undertaken for

field samples representing a wide range of concentrations and

containing a broad spectrum of pigments, revealed a strong

relationship between fluorometric and HPLC values (Fig.

22.4); the maximum variation observed was + 14% (Fig.

22.5).

Such a level of agreement, however, when varying

extraction protocols are employed, would not be expected. In

routine analysis, all participants used a different extraction

procedure for filters in HPLC analysis than for filters in

fluorometric analysis. Typically, the extraction volumes for

filters used in HPLC analysis were significantly lower than

those used for fluorometric analysis. Mode of filter disruption

(if used) also varied. Finally, the contribution of water

retained by the GF/F filter to the total extraction volume was

sometimes not accounted for.

When all data are analyzed in which participants used

their respective extraction protocols, the values determined by

fluorometry ranged from 62% to 142% of the values

determined by HPLC (Fig. 22.5). The larger discrepancies

here, relative to the same comparison for samples in which

extraction was standardized, are not surprising for several

reasons. In addition to the differences in extraction procedure

and manner in which extraction volume was determined, these

results also reflect variability associated with differences in

HPLC injection modes, chlide a reporting practices, and the

ability to resolve (or report) DV chl a.

22.3 CONCLUSIONS

Previous intercalibration studies and methods

comparisons have yielded insights into improved analytical

performance for analysis of chl and other pigments (Latasa et

al. 1996; Bidigare and Trees 2000; Trees et al. 2000, Hooker

et al. in press). Our findings extend these previous efforts.

There were three major findings from this study. First, for the

analysis of a pure chl a standard, fluorometric results were

within 11% of the formulated value while fully automated

HPLC analyses were within 2% of the formulated value.

Second, variability in sample composition, such as the

contribution of DV chl a, can increase variability with both

fluorometric and HPLC analyses. Third, variability in chl a

analysis is further increased when effects of variable

extraction procedures of material on filters and variable modes

of injection and data reporting are incorporated in the

comparison.

Effects Of Variability In Sample Composition

The best agreement between fluorometric and HPLC

analyses will be attained when all chl a products measured by

HPLC are accurately quantified and then summed. This

analysis requires a careful choice of wavelength on the

detector setting to avoid the potential for overestimation of

total chl a when DV chl a is present (Goericke and Repeta

1993, Latasa et al. 1996, Bidigare and Trees 2000).

Effects Of Extraction Procedures

The most accurate analysis of chl a will be derived when

careful attention is given to extraction protocols. Due to the

extremely small volumes of extraction solvent used, small

differences in extraction volume, or variability in water

retention by the filter may have a large impact on the final

calculated value.

Effects Of HPLC Injection Mode

Although the most accurate analysis of chl a requires

modern, automated equipment, improvement in accuracy can

be attained for manual mixed injection systems. As has been

previously recognized (e.g. Mantoura et al. 1997), variability

increases substantially when samples are premixed, then

loaded on the autosampler. By maintaining a constant time

interval between mixing and injecting, variability can be

reduced.

Implications

These findings demonstrated that the most accurate

results were obtained from good instrumentation. A

combination of high quality instrumentation and good

laboratory practices resulted in reduced discrepancy in

reported values relative to formulated values for either

instrumental approach. Attention to detector settings and

injection protocols are important to reduced variability. While

some sources of variability can only be reduced with improved

instrumentation, other sources of variability in reported results

can be reduced through mathematical corrections. Taking the

results from one set of reported values derived from field

analyses, the impacts of these corrections can be shown (Fig.

22.6). In this case, the data were derived from samples

collected on 47 mm GF/F filters, to which 5 ml of 90%
SIMBIOS Project Annual Report

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acetone was added for extraction. As originally reported,

water content contributed by the filters was not accounted for.

Measured water retention by filters of this diameter is

approximately 500 μl. Thus, the reported value was

underestimated on average by 10% due to this factor alone.

Furthermore, these samples were shown (in separate analyses)

to have substantial concentrations of chlide a. By determining

the contribution of chlide a to total chl a in this case, further

improvements in accuracy were attained. These corrections

resulted in final values that were, on average, 95% of the

values independently determined. In summary, attention to

extraction and injection methods, as well as to reporting

practices when chl products are present, will reduce the

variability associated with chl detection by various methods

and will improve the accuracy of chl a results.

ACKNOWLEDGEMENTS

This work was supported by the SIMBIOS Project and the

Office of Naval Research. We thank Dr. Giulietta S. Fargion

and the NASA SIMBIOS Project, Goddard Space Flight

Center, for support as well. The willingness to help in this

endeavor extended by participants from SIMBIOS laboratories

and others is greatly appreciated. This is a contribution from

the University of Maryland Center for Environmental Science.

REFERENCES

Bidigare, R.R. and C.C. Trees. 2000: HPLC phytoplankton

piugments: sampling, laboratory methods, and quality

assurance procedures. Ch. 13, In G.C. Fargion and J.L.

Mueller (eds), Ocean optics protocols for satellite ocean

color sensor validation, rev. 2. NASA Technical

Memorandum –2000-209966.

Clesceri, L.S., A.E. Greenberg, and A.D. Easton (eds). 1992:

Part 10000, Biological Examination, Section 10200H. In

Standard Methods for the examination of water and

wastewater, 20th ed. Baltimore (MD): American Public

Health Assoc, American Water Works Association, Water

Environment Federation.

Goericke, R. and D.J. Repeta. 1993: Chlorophylls a and b and

divinyl chlorophylls in the open subtropical North

Atlantic Ocean. Mar. Ecol. Prog. Ser., 10, 307-313.

Hooker, S.B., H. Claustre, J. Ras, L. Van Heukelem, C. Targa,

and R. Barlow, 2000: The first SeaWifS HPLC analysis

round-robin experiment (SeaHARRE-1). Vol. 14 of

SeaWifS Postlaunch Technical Report Series. Hooker,

S.B. and E.R. Firestone (eds). NASA Technical

Memorandum 104566 (in press).

Jeffrey, S.W., R.F.C. Mantoura, and S.W. Wright. (Eds.)

1997: Phytoplankton pigments in oceanography:

Guidelines to modern methods. Vol. 10, Monographs on

oceanographic methodology, Paris: UNESCO Publishing,

661 pp.

Jeffrey, S.W., S.W. Wright, and M. Zapata. 1999: Recent

advances in HPLC pigment anlaysis of phytoplankton.

Mar. Freshwater Res. , 50, 879-896.

Latasa, M., R.R. Bidigare, M.E. Ondrusek, M.C. Kennicutt II.

1996: HPLC analysis of algal pigments: A comparison

exercise among laboratories and recommendations for

improved analytical performance. Mar. Chem. , 51, 315-

324.

Mantoura, R.F.C., R.G. Barlow, and E.J.H. Head. 1997:

Simple isocractic HPLC methods for chlorophylls and

their degradations products. In Jeffrey, S.W., R.F.C.

Mantoura, and S.W. Wright (eds). Phytoplankton

pigments in oceanography: guidelines to modern

methods. Vol. 10, Monographs on oceanographic

methodology. UNESCO Publishing. 307-326.

Mantoura, R.F.C. and C.A. Llewellyn. 1983: The rapid

determination of algal chlorophyll and carotenoid

pigments and their breakdown products in natural waters

by reverse-phase high-performance liquid

chromatography. Anal. Chim. Acta, 151, 297-314.

Parsons, T.R., Y. Maita, and C.M. Lalli. 1984: A manual of

chemical and biological methods for seawater analysis.

Pergamon Press, New York. 173 pp.

Trees, C.C., D.K. Clark, R.R. Bidigare, M.E. Ondrusek and

J.L. Mueller. 2000: Accessory pigments versus

chlorophyll a concentrations within the euphotic zone: a

ubiquitous relationship. Limnol. Oceanogr., 45, 1130-

1143.

Van Heukelem, L. and C.S. Thomas. 2000: Computerassisted high-performance liquid chromatography method

development with applications to the isolation and

analysis of phytoplankton pigments. J. Chromatogr. A,

(in press).

Wright, S.W., S.W. Jeffrey, R.F.C. Mantoura, C.A. Llewellyn,

T. Bjornland, D. Repeta and N. Welschmeyer. 1991:

Improved HPLC method for the analysis of chlorophylls

and carotenoids from marine phytoplankton. Mar. Ecol.

Prog. Ser., 77, 186-196.
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Figure 22.1 Accuracy associated with the analysis of prepared, identical chl a standards analyzed by participants using their

own analytical methods. In all cases, chl a was used as the calibrant. The percent differences between the values reported by

each participant and the values as formulated are shown for a standard containing only chl a in (A) and for a standard

containing equal portions of DV chl a and chl a in (B). Data is sorted by mode of analysis. In (A) each data point represents

the mean of triplicate analyses. Average variability for fluorometric analyses was 1.5%, for HPLC fully automated analyses

0.8%, for manual mix/ auto-inject analyses 14.2% and for manual mix/ manual inject analyses 7.3%.
SIMBIOS Project Annual Report

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Figure 22.2. Errors in the HPLC analysis of DV chl a when chl a was used as the calibrant. Error varies with wavelength and

bandwidth used. Standards containing only DV chl a (stippled bar) and equal portions of chl a and DV chl a (solid bar) were

used. Only data from laboratories which demonstrated error of ≤ 4% in the analysis of chl a alone were included.

Table 22.1. Injection and analysis configuration for participants employing HPLC in the detection of chl a.

Injection Mode Number of

participants

Type of column employed Reference for mobile phase employed

Fully automated,

refrigerated autosampler 4

C18 (2 investigators)

C8 (1 investigator)

C8 (1 investigator)

Mantoura and Llewellyn (1983)

Van Heukelem and Thomas (in press)

Goericke and Repeta (1993)

Fully automated, nonrefrigerated autosampler 1 C18 Wright et al. (1991)

Manual mix/auto-injection,

Non-refrigerated 2 C18 Wright et al. (1991)

Manual mix/ manual

injection 1

1

C8

C18

Van Heukelem and Thomas (in press)

Wright et al. (1991)
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Figure 22.3. Agreement among laboratories and between fluorometer and HPLC in the analysis of replicate laboratoryprepared filters. All laboratories used their own analytical methods but employed the same procedure for extracting filters.

Chl a values reported by individual laboratories are expressed as the % of the mean consensus HPLC value. Data are sorted by

mode of analysis. Each point represents the mean of 2 analyses (with the exception of two data points).

Figure 22.4. Results of replicate field sample filters of varying concentration analyzed by HPLC and fluorometer by one

laboratory. The same extraction procedure was employed for filters used for HPLC analysis as those used for fluorometric

analysis. In all but four cases, each point compares mean of three replicate filters analyzed by fluorometry and mean mean of

three replicate filters analyzed by HPLC. For reference, the 1:1 line is drawn.
SIMBIOS Project Annual Report

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Figure 22.5. Agreement between replicate field samples of varying concentration analyzed by HPLC and fluorometry. Results of filters

extracted for and analyzed on the fluorometer are compared to results of filters extracted for and analyzed on the HPLC. In the top line, all

filters were extracted using the same procedure and all analyses were performed by one laboratory (Standardized Extraction). In the bottom

line, results were reported by various laboratories employing their own methods for extraction, analysis and reporting (Variable Extraction).

Each point compares the mean of three filters analyzed by fluorometry and the mean of three filters analyzed by HPLC.

Figure 22.6. An example illustrating the ability to correct errors associated with HPLC reporting practices. Data derived from field collected

filters of one participant are presented as percent of the HPLC total chl a values for the same sites derived by a reference laboratory.

Agreement between the participants data and the reference laboratory’s HPLC data is shown for (•) the data as reported, (∆) the data

corrected for extraction volume (water retained in the filter had not been accounted for in the total extraction volume) and ( ) the data

corrected for extraction volumes and contribution of chlide a to the total HPLC chl a value. Each data point compares the mean of three

replicate filters analyzed for each site by the participant to the mean of three replicate filters analyzed by the reference laboratory.
SIMBIOS Project Annual Report

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Chapter 23

Validation of the Water-Leaving Radiance Data Product

Oleg V. Kopelevich, Vladimir I. Burenkov, Svetlana V. Ershova, Sergey V. Sheberstov, Marina A.

Evdoshenko, Genrik S. Karabashev and Constantine A. Pavlov

Ocean Optics Laboratory, P.P.Shirshov Institute of Oceanology, Moscow, Russia

23.1 INTRODUCTION

 The project accomplishments made by SIO RAS group

during the period of 1997-2000 are described below. The goal

of this research was to assess an accuracy of data products

derived from satellite ocean color data including an accuracy

of the atmospheric correction algorithms for variety of

meteorological and oceanological conditions and of the biooptical algorithms for different water cases. A combined

approach was used which included a direct comparison of the

values of water-leaving radiance and chlorophyll

concentration derived from satellite and in situ measured data

as well as numerical simulation and theoretical analysis.

23.2 RESEARCH ACTIVITIES

New Instrumentation

Two new instruments have been constructed for field

studies: a floating spectroradiometer and a portable monitor

photometer (Artemiev et al. 2000a, Kopelevich 1999a). The

floating spectroradiometer measures the continuous spectral

distributions of downwelling irradiance just above sea surface

and of upwelling radiance just beneath sea surface in the

spectral range 390-700 nm with spectral resolution 2.5 nm.

Measurements beneath sea surface allow to eliminate effects

of sun glints and sky light reflected at surface; they are

performed at a distance about 30-50 m from ship to avoid

influence of ship shadow. The monitor photometer was

designed to check calibration of irradiance at 554 nm and

measure continuously the surface irradiance at this wavelength

during spectral measurements by the spectroradiometer. The

radiometric accuracy of the floating spectroradiometer and the

monitor photometer is respectively 5% and 3%.

The calibration was conducted by different ways

including laboratory calibration with a standard lamp and field

calibration with Sun as a standard light source (Sun-shadow

method);also intercalibration of the floating spectroradiometer

and the submersible radiometer MER-2040 was carried out

during Akvanavt’97 cruise (Artemiev et al. 2000b, Kopelevich

1999a).

Field Measurements

Three series of field measurements have been carried out:

• in the Black and Aegean Seas, October 6-24, 1997

(Artemiev et al. 2000b, Kopelevich 1999a);

• in the eastern part of the Barents Sea in AugustSeptember 1998 (Burenkov et al. 2001, Kopelevich

1999a);

• in the eastern part of the Black Sea, September 14-25,

2000.

 The latter was performed at nine drift stations of R/V

Kvant-1 in coastal zone of the Black Sea near Gelendjick

(Table 23.1). The field studies included measurements of the

spectral upwelling radiance and surface irradiance by the

floating spectroradiometer and of vertical profiles of the beam

attenuation coefficient by a submersible transmissometer as

well as sampling and conservation of water samples for

determination of chlorophyll concentration. Two of stations in

Table 23.1 marked by asterisk were performed concurrently

with SeaWiFS measurement at clear-sky conditions and can

be included in the match-up data set; other stations will be

used only for validation of the bio-optical algorithms.

The total number of stations with measurements of the

spectral upwelling radiance and surface irradiance by the

floating spectroradiometer and chlorophyll determinations in

1997-2000 is 36; twelve of them under clear-sky conditions

can be included in SeaWiFS match-up data set, three of them

in the match-up data set for MOS-IRS.

Numerical Simulation

Numerical simulation was performed for modeling the

atmospheric correction and bio-optical algorithms, evaluation

of their error in different situations, and elucidation of reasons

for the occurrence of the errors. The new computer code has

been devised. The atmosphere-ocean system is considered as

a system of plane-parallel layers with the sea surface treated as

a separate layer. The operators of transmittance and

downward and upward reflectivities for the layers are linked

between them by the recursion relations. The computer code

executes an expansion of all the operators in a Fourier cosine
SIMBIOS Project Annual Report

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series over the azimuth angle, discretization over the zenith

angle, solving the algebraic linear equations resulting from the

above discretization, and computing radiances and irradiances.

Solution of the problem for a single uniform layer can be

found by using both the modified discrete ordinate (DISORT)

and Monte Carlo techniques, and the solutions found by

different techniques can be compared.

 The following quantities were computed for SeaWiFS

spectral bands at 412, 443, 490, 510, 555, 670, 765, and 865

nm and different solar and viewing zenith angles θ0 and θ :

• upwelling radiances Lt(θ, θ0) at the top-of-theatmosphere;

• contribution LR(θ, θ0) arising from Rayleigh scattering;

• contribution La(θ, θ0) arising from aerosol scattering

(including the interaction between Rayleigh and aerosol

scattering);

• contribution LW(θ, θ0) arising from backscattering out of

the water;

• upwelling radiances Lu(θ, θ0, 0-

) and Lu(θ, θ0, 0+)

respectively just beneath and above the sea surface;

• upwelling irradiances Eu(θ0, 0-

) just beneath the sea

surface;

• downwelling irradiances Ed(θ0, 0-

) and Ed(θ0, 0+) just

beneath and above the sea surface.

 The computations were made for two contrasting cases:

the Philippine Sea with very clear water and the Barents Sea

with high concentrations of yellow substance and suspended

matter. The subsurface quantities were calculated with

seawater optical characteristics either directly in situ measured

in the Philippine Sea or retrieved from the spectral values of

subsurface radiance reflectance in the Barents Sea; the flat sea

surface and the three-layer atmosphere were asssumed

(Kopelevich et al. 1998).

23.3 RESEARCH RESULTS

Validation of the Atmospheric Correction Algorithm.

The SeaWiFS atmospheric correction algorithm was

validated by direct comparison of the LWN (λi) and τa (λi)

values derived from SeaWiFS data and from in situ

measurements. For the Black and Aegean Seas the agreement

was quite satisfactory for the LWN (λi) and τa (Burenkov et al.

1999, Kopelevich 1999a). For the Barents Sea (Table 23.2)

the SeaWiFS atmospheric correction algorithm resulted to

negative Lwn values at 412 and 443 nm; it underestimated the

Lwn values at 490 nm by 26-71%, and gave rather reasonable

values of Lwn only at 510 and 555 nm. As for chlorophyll

concentration, the SeaWiFS operational algorithm

overestimated its values by the factor of 35-50 (Table 2); the

most part of this error was due to the bio-optical algorithm

which could overestimate chlorophyll concentration more than

twenty times ((Burenkov et al. 2001, Kopelevich 1999b). The

overcorrection of atmospheric effects for the Barents Sea can

be explained by two reasons: first, the assumption of zero

water-leaving radiance in the near-infrared (NIR) is not

correct in turbid waters of the Barents Sea, and second,

increasing error in atmospheric correction under condition of

large solar zenith angle (about 60o

).

We found negative Lwn412 values not only in the Barents

Sea where they were quite common, but also in oligotrophic

waters of the Eastern Mediterranean where the assumption of

zero NIR water reflectance was valid (Kopelevich 1999b).

Our analysis showed that results of the atmospheric correction

depend on the scan angle. Comparison between the Lwn

values derived at small (less than 20o

) and large (greater than

300) scan angles for different spectral bands in the Eastern

Mediterranean showed that the spatially averaged Lwn values

calculated at the small scan angle were higher than the ones at

the large scan angle.

To check an error which can arise from anisotropy of

angular distribution of the water-leaving radiance Lu (θ, 0+

),

the results of our numerical simulation were used. Figure 23.1

shows examples of angular distributions of the upwelling

radiances Lu (θ, 0-

) just beneath the sea surface (dashed lines)

and Lu (θ, 0+

) just above the sea surface (solid lines) for five

SeaWiFS spectral bands and the solar zenith angles θ0=20o

and 40o calculated for the Philippine Sea. It is seen that the

angular distributions of Lu (θ, 0-

) have well-defined anisotropy

whereas it is much less pronounced for the angular

distributions of Lu(θ, 0+

). The reason of such correction of the

angular distribution of the upwelling radiance by the sea

surface is the angular dependence of the reflection coefficient

which is increased with the viewing angle θ. The anisotropy

grows with the solar zenith angle θ0: the ratio of Lu (60o

,

0+

)/Lu(0o, 0+) is equal to 1.02-1.08 for θ0 = 20o

 and 1.11-1.18

for θ0 = 60o

. The obtained results show that the anisotropy of

angular distribution of the water-leaving radiance Lu (θ, 0+

)

can not explain the observed errors in atmospheric correction;

their reason is most likely to be a wrong choice of the aerosol

optical model under observation at large scan angles.

The another manifestation of errors in atmospheric

correction was a false mesoscale structure of the Lwn spatial

distribution (Kopelevich 1999b). Our analysis showed that

such structure was connected with inefficiency of elimination

of cloud effects and could be resulted from scattering by edges

of clouds or stray light within the SeaWiFS sensor or from

high cirrus clouds.

Validation of the Bio-optical Algorithms

Most of our stations can be related to Case 2 waters, and

the SeaWiFS operational algorithm overestimated chlorophyll

concentration. It has been demonstrated that the ratio β

between the absorption coefficients of yellow substance and

phytoplankton pigments at 440 nm can be used as an indicator

of validity of the SeaWiFS bio-optical algorithm (Burenkov et

al. 1999, Kopelevich 1999b). This empirical algorithm is only
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Figure 23.1. Angular distributions of water-leaving radiance just beneath (dashed line) and above (solid line) the sea surface:

the left figures – solar zenith angle 200

 , the right ones- 400.
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Table 23.1. The information on the stations in the Black Sea, September 14-25, 2000.

Date Station Coordinates (degrees) at the

measurement moments

Local time Difference

with GMT, h

09/14/00 1* 44.506 N, 37.837 E 13:50 – 14:20 +4

09/17/00 2 44.485 N, 37.932 E 13:10 – 13:40 +4

09/17/00 3 44.520 N, 37.963 E 15:00 – 15:30 +4

09/17/00 4 44.528 N, 37.973 E 16:10 – 16:30 +4

09/18/00 5 44.526 N, 37.994 E 11:40 – 12:05 +4

09/18/00 6 44.500 N, 37.975 E 12:30 – 13:00 +4

09/18/00 7* 44.462 N, 37.964 E 13:50 – 14:25 +4

09/23/00 8 44.491 N, 37.943 E 13:50 – 14:30 +4

09/25/00 9 44.485 N, 37.967 E 13:50 – 14:30 +4

Table 23.2 The values of the water-leaving radiances (W⋅m-2⋅μ-1⋅sr-1), aerosol optical thickness τa

865, and chlorophyll

concentration (mg⋅m-3) for two stations in the Barents Sea measured and derived from SeaWiFS data; ∆, % is the

relative discrepancy between derived and measured data.

λ, nm St.1112 St.1131

LWN

SeaWiFS

LWN

measured

∆ LWN

SeaWiFS

LWN

measured

∆

412 -0.66 0.088 - -0.671 0.145 -

443 -0.226 0.18 - -0.255 0.225 -

490 0.288 0.387 -26% 0.151 0.350 -71%

510 0.430 0.472 -9% 0.224 0.336 -33%

555 0.686 0.681 +0.8% 0.181 0.243 -25%

τa

865 0.050 0.045 +11% 0.065 0.055 +18%

• • • ,

mg⋅m3

20.7 0.42 +50 times 3.3 0.09 +35 times
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Table 23.3. Comparison between chlorophyll-a concentrations (mg⋅m-3) measured and retrieved by SIO RAS and

operational SeaWiFS algorithms at different stations in the Barents Sea. ∆, % is the relative error; β is the ratio between

the absorption coefficients of yellow substance and phytoplankton pigments at 440 nm.

St. Coordinates Chl

measured

Chl

SIORAS

∆

SIORAS

Chl

SeaWiFS

∆

SeaWiFS

β

1088 70.42 N, 47.58E 0.16 0.24 52 0.63 300 8.2

1090 70.18N, 52.42E 0.50 0.56 12 3.3 540 14.9

1095 68.97N, 58.47E 0.79 0.34 57 9.9 1200 20.9

1112 69.09N, 58.29E 0.42 0.46 9 9.5 2170 35.9

1123 69.50N, 57.25E 0.38 0.55 45 4.8 1160 22.9

1126 69.67N, 57.24E 0.18 0.114 37 2.7 1390 25.5

1131 69.77 N, 56.28 E 0.091 0.038 58 1.01 1010 14.7

1157 70.54N, 52.79E 0.25 0.14 43 1.09 340 8.7

1174 69.25N, 41.00E 1.39 0.92 34 1.0 28 1.6

1183 71.50N, 41.00E 0.38 0.38 0.5 0.81 113 3.2

1196 74.75N, 41.00E 0.13 0.14 8 0.28 115 4.1

1209 78.00 N, 41.00E 0.16 0.17 3 0.25 56 3.0

1281 76.00N, 42.27E 0.27 0.38 40 0.44 63 3.2

applicable to Case 1 waters and assumes an existence of

definite relationship between the absorption by phytoplankton

pigments and absorption by yellow sunstance. According to

results of our analysis, the SeaWiFS algorithm overestimates

chlorophyll concentration if the ratio β exceeds the critical

value which is about 2. In the Black and Aegean Seas where

the β values are 2.6-5.9 the SeaWiFS algorithm overestimates

the chlorophyll concentration about twice. The refined results

for the Barents Sea are presented in Table 23.3. As it is seen,

in the Pechora Basin (St. 1090-1157), which is under strong

influence of the Pechora river discharge, the β values are 8.7-

35.9, and the SeaWiFS algorithm overestimates the

chlorophyll concentration by a factor of 4.4-22.7.

 The semianalytic algorithm based on an analytic formula

for the water radiance reflectance and low-parametric models

for seawater absorption and backscattering coefficients has

been developed. As it is seen from Table 23.3, it provides a

reasonable agreement between the calculated and measured

chlorophyll concentration: relative errors are equal to 9-57%

for stations in the Pechora Basin and 0.5-52% for other

stations; for stations in the Black and Aegean Seas the errors

were within 2-33% (Burenkov et al. 1999, Kopelevich 1999b).

The semianalytic algorithm allows to retrieve not only

chlorophyll concentration but also the yellow substance

absorption and particle backscattering coefficients under

assumption that their spectral dependencies are known. In this

case we have three unknowns ag, bbp and ca and five

equations (with SeaWiFS spectral bands 412, 443, 490, 510,

and 555 nm) to find these unknowns. Our sensitivity studies

have showed that a correct choice of the slope parameters S

and n of spectral dependencies of the yellow substance

absorption and particle backscattering coefficients (especially

of the former) is of critical importance in the retrieval of ag,

bbp and ca. in the Barents Sea waters with high concentrations

of yellow substance and suspended matter. A special

procedure to assess regional S-values, using results of the field

studies, has been developed, and the relationship between the

S-parameter and the band ratio r (412)/r (490) has been found

(Burenkov et al. 2001). The mean error in determining the Sparameter is equal to ∼0.001 nm-1; and our analysis showed

that effect of this error on the error in chlorophyll

concentration depends on the β-ratio between the absorption

coefficients of yellow substance and phytoplankton pigments.

The errors in chlorophyll concentration are within 18-40% if
SIMBIOS Project Annual Report

162

β < 5, but the errors increase for higher values of β (Burenkov

et al. 2001).

 Our studies showed that the semianalytic algorithm can

solve the problem of Case 2 waters but it places much more

severe requirements to atmospheric correction than the

SeaWiFS operational algorithm, and improvement of

atmospheric correction for Case 2 waters is of paramount

importance.

REFERENCES

Artemiev, V.A., Burenkov, V.I., Vortman, M.I., Grigoriev,

A.V., Kopelevich, O.V., and Khrapko, A.N., 2000a: Seatruth measurements of ocean color: a new floating

spectroradiometer and its metrology. Oceanology, 40,

139-145. Translated from Okeanologiya, 40, No.1, 2000,

pp.148-155.

Artemiev, V.A., Burenkov, V.I., Woznyak, S.V., Grigoriev,

A.V., Daretsky, M., Demidov, A., Kopelevich, O.V.,

Frantsuzov, O.N., and Khrapko, A.N., 2000b: Sea-truth

measurements of ocean color: Field studies in the Black

and Aegean Seas. Oceanology, 40, 177-182. Translated

from Okeanologiya, 40, No.2, 2000, pp.192-198.

Burenkov V.I., Kopelevich O.V., Sheberstov S.V., Ershova

S.V., Evdoshenko M.A., 1999: Bio-optical characteristics

retrieved from satellite ocean color data. The Eastern

Mediterranean as a Laboratory Basin for the Assessment

of Contrasting Ecosystems. P.Malanotte-Rizzoli and

V.N.Eremeev (eds), Kluwer Academic Publishers,

Netherlands. P. 313-326.

Burenkov V.I., Kopelevich O.V., Sheberstov S.V., and

Vedernikov, V.I., 2000: Sea-truth measurements of ocean

color: Validation of the SeaWiFS satellite scanner data.

Oceanology, 40, 329-334. Translated from Okeanologiya,

40, No.3, 2000, pp.357-362.

Burenkov, V.I., Vedernikov, V.I., Ershova, S.V., Kopelevich,

O.V., and Sheberstov, S.V., 2001: Use of SeaWiFS data

for assessment of bio-optical characteristics of the Barents

Sea. Oceanology (in press).

Kopelevich, O.V., D.B. Rogozkin, and S.V. Sheberstov, 1998:

Results of application of the new atmospheric correction

method to the Pacific ocean color simulated data set.

1998. Proceedings of PORSEC’98-Qingdao, July 28-31,

Qingdao, China, 1, 54-58.

Kopelevich, O.V., 1999a: Validation of the water-leaving

radiance data product. NASA/TM-1999-208645, 88-93.

Kopelevich, O.V., 1999b: Validation of the water-leaving

radiance data product. NASA/TM-1999-209486, 121-

126.
SIMBIOS Project Annual Report

163

GLOSSARY

ACE Aerosol Characterization Experiment

ADEOS Advanced Earth Observation Satellite

AERONET Aerosol Robotic Network

AM-1 Not a acronym, used to designate the

morning platform of EOS

AOP Apparent Optical Properties

AOT Aerosol Optical Thickness

APV Autonomous Profiling Vehicle

ARGOS Not an acronym, but the name given to the

data collection and location system on the

NOAA Operational Satellites.

ASCII American Standard Code for Information

Interchange

AVHRR Advanced Very High Resolution

Radiometer

AVIRIS Advanced Visible and Infrared Imaging

Spectrometer

BATS Bermuda Atlantic Time-series Study

BBOP Bermuda Bio-Optics Profiler

BBSR Bermuda Biological Station for Research

BNL Brookhaven National Laboratory

BTM Bermuda Test Mooring

Cal/Val Calibration and Validation

CalCOFI California Cooperative Oceanic

Fisheries Investigation

CALVAL Calibration Validation

CARICO Carbon Retention in a Colored Ocean

Case-1 Water whose reflectance is determined by

absorption.

Case-2 Water whose reflectance is significantly

influenced by scattering.

CCD Charge-Coupled Device

CDOM Chromophoric Dissolved Organic Matter

CHN Carbon, Hydrogen and Nitrogen

CHORS Center for Hydro-Optics and Remote

Sensing

CICESE Centro de Investigation Cientifica y de

Edcacion Superior de Ensenada

CIMEL The name of a sun photometer

manufacturer

CNES Centre National d’Edudes Spatialle

CONICIT Consejo Nacional de Investigaciones

Cientifica y Technologicas (Venezuela)

CRAM Conditional Relaxation Analysis Method

CTD Conductivity-Temperature-Depth

CZCS Coastal Zone Color Scanner

DAAC Distributed Active Archive Center

DLR Deutshe Forshungsanstalt f r Luft-und

Raumfarhrt (German Aerospace Center)

DMF Dimethylformamide

DOC Dissolved Organic Carbon

DoD Department of Defense

DOE Department of Energy

ECOHAB Ecology of Harmful Algal Blooms

EEZ Exclusive Economic Zone

EORC Earth Observation Research Center

EOS Earth Observing System

FFP Firm-Fixed Price

FOV Field of View

ftp File transfer protocol

FWHM Full Width Half Maximum

GAC Global Area Coverage, coarse resolution

satellite data with a nominal ground resolution at nadir of approximately 4 Km

GB Gigabyte, or about one billion bytes

GF/F Not an acronym, but a specific type of glass

fiber manufactured by Whatman.

GLI Global Imager

GoCal Gulf of California

GPS Global Positioning System

GSFC Goddard Space Flight Center

HIVE High-Latitude Intercomparison and

Validation Experiment

HOBI Hydro-Optics, Biology and Instrumentation

HOT Hawaii Ocean Time series

HPLC High Performance Liquid Chromatography

HQ Headquarters

HRPT High Resolution Picture Transmission

ICESS Institute for Computational Earth

Science System

IDL Interactive Data Language

INDOEX Indian Ocean Experiment

IOCCG International Ocean Color Coordinating

Group

IOP Inherent Optical Properties

IR Infrared

IRS Indian Remote Sensing Satellite

ISCCP International Satellite Cloud

Climatology Project

ISPO Interim SIMBIOS Project Office

JGOFS Joint Global Ocean Flux Study

JPL Jet Propulsion Laboratory

LAC Local Area Coverage, fine resolution

satellite data with a nominal ground resolution at nadir of approximately 1Km

LIDAR Light Detection and Ranging Instrument

LED Light Emitting Dicode

LOA Laboratorie d’Optique Atmospherique

MARMAP Marine Resources Monitoring, Assessment

and Prediction

MBARI Monterey Bay Aquarium Research Institute

MB Megabyte, or about one million bytes

MER Multispectral Environmental Radiometer

MERIS Medium Resolution Imaging Spectrometer

MFRSSR Marine Fast Rotating Shadow-band

Spectral Radiometer

MISR Multi-angle Imaging SpectroRadiometer

MOBY Marine Optical Buoy
SIMBIOS Project Annual Report

164

MODAPS Modular Ocean Data and Power System

MODIS Moderate Resolution Imaging

Spectroradiometer

MOS Modular Optoelectronic Scanner

MSL12 Multi-Sensor level-1B to level-2 code

MTPE Mission to Planet Earth

NASA National Aeronautics and Space

Administration

NASDA National Space Development Agency

of Japan

NAVOCEANO Naval Oceanographic Office

NDBC National Data Buoy Center

NIMBUS Not an acronym, but a series of NASA

experimental weather satellites containing a

wide variety of atmosphere, ice, and ocean

sensors.

NIST National Institute of Standards and

Technology

NOAA National Oceanic and Atmospheric

Administration

NODC National Oceanographic Data Center

NOPP NIMBUS Observation Processing System

NRA NASA Research Announcement

NSF National Science Foundation

NSPO National Space Program Office

NTOU National Taiwan Ocean University

OCI Ocean Color Imager

OCTS Ocean Color and Temperature Sensor

ONR Office of Naval Research

ORCA Optical Research Consortium of the Arctic

OSC Orbital Sciences Corporation

OSU Oregon State University

PAR Photosynthetically Available Radiation

PHILLS Hyperspectral Imager for Low Light

Spectroscopy

PI Principal Investigator

PM-1 Not a acronym, used to designate the

afternoon platform of EOS

POLDER Polarization Detecting Environmental

Radiometer (France)

PRR Profiling Reflectance Radiometer

R&D Research and Development

R/V Research Vessel

ROCSAT Republic of China (Taiwan) Satellite

platform for the OCI sensor.

RR Round-Robin

SAB South Atlantic Bight

SBE Sea-Bird Electronics

SCOR Scientific Committee on Oceanic Research

SDDC Science Data Distribution Center

SeaOPS SeaWiFS Optical Profiling System

SeaBAM SeaWiFS Bio-optical Algorithms Miniworkshop

SeaBASS SeaWiFS Bio-optical Archive and Storage

System

SeaDAS SeaWiFs Data Analysis System

SeaWiFS Sea-viewing Wide Field-of-view Sensor

SIMBAD The name of a sun photometer

SIMBIOS Sensor Intercomparison and Merger for

Biological and Interdisciplinary Oceanic

Studies

SIO Scripps Institution of Oceanography

SGI Silicon Graphics, Inc.

SIRREX SeaWiFS Intercalibration Round-Robin

Experiment

SLOWDROP Slow Descendent Rate Optical Profiler

SMSR SeaWiFS Multispectral Surface

Reference

SOOP Ship of Opportunity Program

SOW Statement of Work

SPMR SeaWiFS Profiling Multi-channel

Radiometer

SQM SeaWiFS Quality Monitor

ST Science Team

TB Terabytes

TOA Top of the Atmosphere

TOMS Total Ozone Mapping Spectrometer

TOTO Tongue of the Ocean

TSM Total Suspended Matter

TSRB-II Tethered Spectral Radiometer Buoy - II

XBT Expendable Bathythermograph