NASA/TM-2003

SIMBIOS Project 2003 Annual Report 

Giulietta S. Fargion, Science Applications International Corporation, Maryland 

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

National Aeronautics and 

Space Administration 

Goddard Space Flight Center 

Greenbelt, Maryland 20771 

December 2003
SIMBIOS Project Annual Report 

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 biooptical 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-99 funded research. The contributions are published as submitted, 

with the exception of minor edits to correct obvious grammatical or clerical errors. 

ii 
SIMBIOS Project Annual Report 

Table of Contents 

1. AN OVERVIEW OF SIMBIOS PROGRAM ACTIVITIES AND ACCOMPLISHMENTS .........................................1

2. SIMBIOS: SCIENCE TEAM AND CONTRACTS ...........................................................................................34

3. ADAPTATION OF A HYPERSPECTRAL ATMOSPHERIC CORRECTION ALGORITHM FOR MULTI-SPECTRAL 

OCEAN COLOR DATA IN COASTAL WATERS ................................................................................................35

4. BIO-OPTICAL MEASUREMENTS IN UPWELLING ECOSYSTEMS IN SUPPORT OF SIMBIOS .........................41

5. SATELLITE OCEAN-COLOR VALIDATION USING SHIPS OF OPPORTUNITY.................................................52

6. MERGING OCEAN COLOR DATA FROM MULTIPLE MISSIONS ...................................................................73

7. BIO-OPTICAL AND REMOTE SENSING OBSERVATIONS IN CHESAPEAKE BAY ...........................................84

8. REFINEMENT OF PROTOCOLS FOR MEASURING THE APPARENT OPTICAL PROPERTIES OF SEAWATER .....98

9. OPTIMIZATION OF OCEAN COLOR ALGORITHMS: APPLICATION TO SATELLITE AND IN SITU DATA 

MERGING. ..................................................................................................................................................114

10. THE MARINE FAST-ROTATING SHADOW-BAND NETWORK: STATUS REPORT AND NEW RETRIVAL 

TECHNIQUES ..............................................................................................................................................124

11. BIO-OPTICAL MEASUREMENT AND MODELING OF THE CALIFORNIA CURRENT AND SOUTHERN 

OCEANS..................................................................................................................................................131

12. VARIABILITY IN OCEAN COLOR ASSOCIATED WITH PHYTOPLANKTON AND TERRIGENOUS 

MATTER: TIME SERIES MEASUREMENTS AND ALGORITHM DEVELOPMENT AT THE FRONT SITE 

ON THE NEW ENGLAND CONTINENTAL SHELF. ..........................................................................................138

13. OCEAN OPTICS PROTOCOLS AND SIMBIOS PROTOCOL INTERCOMPARISON ROUND ROBIN 

EXPERIMENTS (SPIRREX).........................................................................................................................145

14. BERMUDA BIO OPTICS PROJECT...........................................................................................................147

15. PLUMES AND BLOOMS: MODELING THE CASE II WATERS OF THE SANTA BARBARA CHANNEL...........153

16. ALGORITHMS FOR PROCESSING AND ANALYSIS OF OCEAN COLOR SATELLITE DATA FOR 

COASTAL CASE 2 WATERS.........................................................................................................................160

17. VARIED WATERS AND HAZY SKIES: VALIDATION OF OCEAN COLOR SATELLITE DATA PRODUCTS 

IN UNDER-SAMPLED MARINE AREAS: II.....................................................................................................175

18. HPLC PIGMENT MEASUREMENTS FOR ALGORITHM DEVELOPMENT AND VALIDATION IN SUPPORT 

OF THE SIMBIOS SCIENCE TEAM .............................................................................................................185

19. ASSESSMENT, VALIDATION, AND REFINEMENT OF THE ATMOSPHERIC CORRECTION ALGORITHM 

FOR THE OCEAN COLOR SENSORS ............................................................................................................193

iii 
SIMBIOS Project Annual Report 

Chapter 1 

An Overview of SIMBIOS Program Activities and 

Accomplishments 

Giulietta S. Fargion 

 Science Applications International Corporation (SAIC), Beltsville, Maryland 

Charles R. McClain 

NASA Goddard Space Flight Center, Greenbelt, Maryland 

1.1 INTRODUCTION 

The SIMBIOS Program was conceived in 1994 as a result of a NASA management review of the 

agency's strategy for monitoring the bio-optical properties of the global ocean through space-based ocean 

color remote sensing. At that time, the NASA ocean color flight manifest included two data buy missions, 

the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) and Earth Observing System (EOS) Color, and 

three sensors, two Moderate Resolution Imaging Spectroradiometers (MODIS) and the Multi-angle 

Imaging Spectro-Radiometer (MISR), scheduled for flight on the EOS-Terra and EOS-Aqua satellites. The 

review led to a decision that the international assemblage of ocean color satellite systems provided ample 

redundancy to assure continuous global coverage, with no need for the EOS Color mission. At the same

time, it was noted that non-trivial technical difficulties attended the challenge (and opportunity) of 

combining ocean color data from this array of independent satellite systems to form consistent and accurate 

global bio-optical time series products. Thus, it was announced at the October 1994 EOS Interdisciplinary

Working Group meeting that some of the resources budgeted for EOS Color should be redirected into an 

intercalibration and validation program (McClain et al., 2002). 

NASA Goddard Space Flight Center (GSFC) was directed to develop an intercalibration and validation 

program plan for submission to NASA Headquarters (HQ) by May 1995. This plan envisioned a Science 

Team funded by a NASA Research Announcement (NRA) (released in July 1996) and the SIMBIOS 

Project Office that was established at GSFC in January 1997. The initial SIMBIOS Program was scoped 

for five years (1997-2001) and included separate support for a science team and the Project Office. Dr. 

Mueller (San Diego State University) acted as an interim project manager at GSFC under a one-year 

assignment to assist in getting the project office organized and the science team contracts executed. During 

the second year of the SIMBIOS Project, Dr. McClain assumed project management for SeaWiFS and 

SIMBIOS, as both Dr. Cleave and Dr. Mueller stepped down in their roles as project managers of these two 

projects, respectively. In fall 1998, Dr. Fargion was hired as Deputy Project Manger to assist Dr. McClain. 

In September 2000, Dr. McClain assumed new responsibilities in assisting HQ to develop a long-term

program for global carbon cycle research. As a result, SeaWiFS and SIMBIOS Project Office were 

reorganized somewhat to allow Dr. McClain to focus on the carbon initiative. Dr. Feldman assumed 

management responsibilities for SeaWiFS and Dr. Fargion for SIMBIOS, respectively. Due to the success 

of the SIMBIOS Program combined with a strong collaboration with the US and international ocean 

communities, HQ release a second NRA (1999) and granted an extension of three years to the project. 

However, in 2002, NASA HQ decided to discontinue the program in its present form. The rationale

centered on three considerations. The first was a desire by HQ to integrate the various ocean color 

calibration and validation activities of the SIMBIOS, SeaWiFS, and the MODIS programs under a common 

ocean color team which would also include investigators supported under the NASA Ocean 

Biogeochemistry program. While the three ocean color projects have separate management and funding 

structures, they have been coordinated and mutually supportive with little redundancy. The second 

consideration stems from initial problems with MODIS ocean data quality and accessibility which has 

made it imperative for NASA to focus its available resources on MODIS ocean calibration and validation. 

1 
SIMBIOS Project Annual Report 

The third consideration is the preparation for ocean color observations from the Visible-Infrared 

Radiometer Suite (VIRIS) on National Polar Orbiting Environmental Satellite System (NPOESS) 

Preparatory Project (NPP), which is scheduled to launch in 2006. 

The SIMBIOS Project Office, co-located with the SeaWiFS Project Office, provides support and 

coordination for the SIMBIOS Program, such as administration, project documentation, and interagency

and international coordination. It also incorporates aspects of post launch calibration and characterization, 

in situ data collection, protocol developments, round robins, algorithm development and evaluation, 

product merging, and data processing (Figure 1.1 and Table 1.1). All components illustrated in Figure 1.1 

are tightly connected (and in some cases overlap) and were thoroughly developed and integrated by the 

Project during the operational years (1997-2003). 

Figure 1.1: SIMBIOS Project activity areas 

The specific proposed objectives of the SIMBIOS Program were: (1) to quantify the relative accuracy 

of measurements from the ocean color products from each mission, (2) to work with each project to 

improve the level of confidence and compatibility among these products, and (3) to develop methodologies 

for generating merged level-3 products. These objectives became operational. In particular, the 

intercomparison component performs many functions similar to those being performed by each individual 

mission (calibration, validation, quality control, algorithm development and data processing) but does so by

integrating information from each project, augmenting activities where required, providing feedback to 

each project and the ocean color community, and coordinating with the international community. The 

program requirements were identified as:

2 
SIMBIOS Project Annual Report 

• Field measurement and data processing protocol definition and development

• Global bio-optical and atmospheric in situ data collection

• Bio-optical and atmospheric database development (SeaBASS) 

• Traceability of laboratory calibration sources to standards (round robin) 

• Instrumented calibration sites (MOBY) 

• Prelaunch sensor calibration and characterization protocols 

• On-orbit calibration evaluation and methodology development

• Bio-optical and atmospheric correction algorithm development 

• Product accuracy evaluation and methodology development 

• Data merger algorithm development and data processing 

• High volume data processing capabilities 

• Technology evaluation and development (SQM) 

• Multi satellite data processing software 

• Systematic documentation (NASAtechnical memorandum and publications) 

SIMBIOS has worked with several missions, such as SeaWiFS, Ocean Color and Temperature Scanner 

(OCTS), Polarization and Directionality of the Earth's Reflectances (POLDER), Modular Optoelectronic 

Scanner (MOS), Ocean Scanning Multispectral Imager (OSMI), MODIS (Terra and Aqua), Medium

Resolution Imaging Specrometer (MERIS) and Global Imager (GLI). The Project staff ensured the 

development of internally consistent research products and time series from multiple satellite ocean color 

data sources; developed methodologies for cross-calibration of satellite ocean color sensors; developed 

methodologies for merging data from multiple ocean color missions; promoted cooperation between ocean 

color projects, and served as a prototype for future Earth observation programs. In order to better 

communicate with the community, the project held annual open science team meetings and workshops, 

participated as a member in several mission teams, documented all activities in NASA technical 

memorandum (TM) and hosted a web site organized to serve as the main information resource of Project

activities, the Project Office, and the Science Team. The Project Office, in an effort to educate and promote 

the concept of an organized program of sensor cross-calibration and validation, has sent representatives to

several international conferences. The objectives, and activities of SIMBIOS are discussed in more detail in

a number of documents including the SIMBIOS Project Annual Reports (McClain and Fargion, 1998 & 

1999; Fargion and McClain 2000, 2001 & 2002), the Ocean Optics Protocols TMs (Mueller & Fargion, 

2003; Fargion et al., 2001) and round robin reports (Riley and Bailey 1998; Meister et al., 2002 & 2003;

Van Heukelem et al. 2002) and others cited in this chapter. 

Table 1.1: SIMBIOS Project Activities and Responsible Staff 

• Satellite Data Processing

Bryan Franz, Joel Gales, Sean Bailey and SeaWiFS staff

• Satellite Characterization

Bob Barnes and Gerhard Meister

• Data Merging 

Ewa Kwiatkowska-Ainsworth and Bryan Franz

• Support Services 

Sean Bailey, Jeremy Werdell, Christophe Pietras and Kirk Knobelspiesse 

• Data Product Validation 

Sean Bailey, Jeremy Werdell, Christophe Pietras and Kirk Knobelspiesse 

• Calibration Round Robin 

Gerhard Meister and Bob Barnes 

1.2 SIMBIOS SCIENCE TEAM 

The SIMBIOS Science Team was selected through NRA’s 1996 and 1999. NASA HQ manages the 

process of team selection, but the GSFC NASA Procurement Office handles the team contracts, work 

3 
SIMBIOS Project Annual Report 

statements and, if necessary, budget negotiations. The Project funds numerous US investigators and 

collaborates with several international investigators, space agencies [e.g., National Space Development 

Agency of Japan (NASDA), European Space Agency (ESA), Korea Aerospace Research Institute (KARI)] 

and international organizations [e.g., International Ocean Colour Coordinating Group (IOCCG), Joint 

Research Center (JRC)]. US investigators under contract provide in situ atmospheric and bio-optical data

sets, and develop atmospheric correction and bio-optical algorithms and methodologies for data merger 

schemes. NASA Procurement requires formal evaluations for all contracts at the end of each contract year. 

These evaluations go into a database and are shared with the PI’s institution or upper management. The 

locations of specific SIMBIOS team investigations are shown in Figures 1.2 and 1.3. 

The international ocean color community’s response to NRA-99 was overwhelming: a total of 75 PI’s 

proposed collaborations with the Project. The twelve international proposals covered topics ranging from

protocols, calibration-validation activities, atmospheric-biological algorithms, and data merging. The 

SIMBIOS Science Team meetings were held in August 1997 at Solomons Island (Maryland), in September 

1998 at La Jolla (California), in September 1999 at Annapolis (Maryland), in January 2001 at GSFC

(Maryland), and in January 2002 at Baltimore (Maryland). SIMBIOS Science meetings had large US and 

international contingents, including participants from space agencies and international organizations. 

During each year, the Project has hosted several US and international visiting scientists (e.g., Dr. Antoine, 

Dr. Deschamps, Dr. Frouin, Dr. Fukushima, Dr. Hagolle, Dr. Kopelevich, Dr. Miller, Dr. Nicolas, Dr. 

Souaidia, Dr. Subramaniam, Dr. Tanaka, Dr. Yamamoto and Dr. Zibordi), staying from 2 weeks to one 

year at GSFC. 

Figure 1.2: 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. 

4 
SIMBIOS Project Annual Report 

Figure 1.3: 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): Gregg; Hooker; Maritorena; Mueller; Trees and Wang. 

International: Bohm; Zibordi; Fougnie; Deschamps; Antoine; Kopelevich; Ishizaka; Fukushima; Chen; Li & 

Lynch. 

1.3 OCEAN COLOR SATELLITE DATA PROCESSING 

Over the past several years, the SIMBIOS Project has been engaged in the characterization, validation, 

intercomparison, and cross-calibration of a host of space-borne ocean color sensors (Table 1.2 and Table 

1.3). This work has included the characterization and calibration of OCTS and POLDER (Wang et al., 

2002), the cross-calibration and long-term intercomparison of MOS and SeaWiFS (Franz et al., 2001;

Wang anf Franz, 2000), the cross-calibration of OSMI to SeaWiF (Franz and Kim, 2001), and the 

intercomparison of MODIS/Terra (Fargion and McClain, 2003b) and MODIS/Aqua with SeaWiFS, as well

as independent validation of MODIS (Terra & Aqua), SeaWiFS, OCTS, and POLDER using coincident in

situ measurement archived in SeaWiFS Bio-optical Archive and Storage System (SeaBASS). 

To facilitate the processing and intercomparison of ocean color products from multiple instruments, the 

SIMBIOS Project developed atmospheric correction code based on the SeaWiFS algorithm of Gordon and 

Wang (1994). The approach was to identify those few parts of the algorithm that were sensor or band-pass 

specific, and develop a software package that could process data from multiple ocean color sensors with 

minimal changes in the algorithms. The sensitivity of the atmospheric correction algorithm to differences in

spectral bands was carefully assessed by Wang (1999), wherein he showed by simulation that these 

differences could be accurately accounted for through exact calculation of the Rayleigh reflectances and 

minor modifications to the diffuse transmittance calculation. The multi-sensor level-1 to level-2 code

(MSL12) has been used to process data from MOS, OCTS, POLDER, OSMI, and SeaWiFS, with support

5 
SIMBIOS Project Annual Report 

for MODIS currently underway. Due to its enhanced flexibility and maintainability, the software was 

adopted by the SeaWiFS Project for all standard production, calibration, and validation activities and is 

widely distributed and utilized by the international ocean color community through the SeaWiFS Data 

Analysis System (SeaDAS). 

Table 1.2: Satellite data supported (1997-2003). 

 

• MOS-SeaWiFS cross calibration and data merger with SeaWiFS 

– German Aerospace Research Establishment (DLR) 

• MOS data acquisition at NASA Wallops Flight Facility 

– Indian Space Research Organization (ISRO) 

• OCTS-POLDER cross calibration 

– National Space Development Agency of Japan (NASDA) 

– Centre National d’Etudes Spatiales (CNES), France 

• OCTS global GAC reprocessing 

– NASDA 

• OSMI-SeaWiFS cross calibration and data processing 

– Korean Aerospace Research Institute (KARI) 

• SeaWiFS calibration and product validation 

• MODIS (Terra & Aqua) product validation 

• MODIS (Terra & Aqua) data merger with SeaWiFS (9km Chlorophyll product and diagnostic

data set) 

• MERIS calibration and product validation (MOBY) 

– European Space Agency (ESA) 

• GLI characterization and field calibration cruise 

- NASDA 

Table 1.3: Work done by sensor (1997-2003). 

 Satellite Characterization in situ matchups Lw, & chlorophyll MOBY AOT 

MOS √ √

OCTS √ √

POLDER √ √

SeaWiFS √ √ √ √

OSMI √ √ √

GLI √ 

 MODIS (Terra) √ √ √ √

 MODIS (Aqua) √ √ √ √

The SIMBIOS Project, using MSL12, performed an independent vicarious calibration of the OCTS 

sensor, then reprocessed the entire global OCTS mission archive (Figure 1.4). This data was distributed 

through the Goddard DAAC, with processing and display support distributed through SeaDAS. This

provided the ocean color community with a nearly complete, highly compatible ocean color time-series 

from September 1996 to the present day, including the first complete record of an El Niño/La Nina event.

The SIMBIOS Project also developed and evaluated methods for cross-calibration of ocean color sensors, 

beginning with the MOS and SeaWiFS instruments. The MOS is a German ocean color sensor flying on the 

Indian Space Agency IRS-P3 satellite. Because the sensor has a small swath width of 192 km and only 

four ground stations capable of receiving the real time-only transmissions, there is limited opportunity for 

ground truth. In addition, the sensor is a push-broom CCD array with 384 independent detectors in each 

band, so calibration requires both a cross-scan, detector-relative calibration and an absolute calibration. As 

a prototype for future missions, the SIMBIOS Project developed a technique to vicariously calibrate MOS 

6 
SIMBIOS Project Annual Report 

using SeaWiFS (Wang and Franz, 2000). The method is analogous to that used by the SeaWiFS Project for 

vicarious calibration to MOBY, except that for MOS the Project used SeaWiFS observations rather than 

MOBY measurements as truth. A major advantage of this technique is that the Project was able to obtain a 

large number of matchups for each detector of each band, and thus derive both the absolute and detectorrelative calibrations simultaneously. This vicarious cross-calibration, coupled with the instrument

calibration performed by the MOS Project, has enabled the retrieval of oceanic optical properties which 

maintain good agreement with SeaWiFS, even after three years(Wang et al., 2001). 

The SIMBIOS Project performed a similar cross-calibration between SeaWiFS and OSMI (Franz and 

Kim, 2001). OSMI is a whisk-broom scanning CCD array with 96 detectors per band distributed along 

track. The detectors are divided into two independently amplified fields of 48-detectors each. Prior to 

cross-calibration, the calibrated level-1B radiances exhibited significant interdetector striping, banding 

between the detector fields, and absolute radiances that were below the predicted Rayleigh radiance in most

bands. As such, it was not even possible to compute oceanic optical properties, as any atmospheric 

correction would fail. In this case, it was necessary to develop a calibration for the NIR channels as well as 

the visible bands. The NIR calibration was performed using the SeaWiFS retrieved aerosol optical

thickness as truth, using a technique originally developed to calibrate SeaWiFS to in situ aerosol optical 

thickness measurements (Franz et al., 2001). Once the NIR calibration was established, the calibration of 

the visible bands proceeded in a manner analogous to the MOS-to-SeaWiFS cross-calibration, with 

independent gains derived for each detector of each band. After this cross-calibration it was possible, for 

the first time, to process an OSMI scene and retrieve meaningful oceanic optical properties. 

The final year of the SIMBIOS Project was largely dedicated to MODIS analyses. An extensive 

evaluation of the temporal trends in several standard ocean color products derived from SeaWiFS and 

MODIS/Terra was performed to determine the long-term relative stability between the two sensors and to 

develop an understanding of their similarities and differences. This time-series analysis investigated 

variations in the mean value of water-leaving radiance and chlorophyll products over the period from

March 2000 through December 2002, for both global and regional geographic areas, between MODIS 

Collection #4 and SeaWiFS Reprocessing #4. The analysis was able to demonstrate the remarkable 

temporal stability of SeaWiFS, and assess the long-term stability of MODIS/Terra (see 

http://simbios.gsfc.nasa.gov/ simbios_modis.html; Fargion & MCClain, 2003b). This work on these various 

sensors has provided critical insight into the issues and limitations associated with the establishment of a 

long-term, multi-mission, climate data record for ocean color. 

Figure 1.4: Retrospective satellite data processing reprocessing of OCTS GAC mission archive 

7 
SIMBIOS Project Annual Report 

1.4 DATA MERGER 

The objective of ocean color data merger is to create a consistent series of systematic ocean color 

measurements from multi-instrument, multi-platform and multi-year observations based on accurate and 

uniform calibration and validation over the lifetime of the measurement. The most obvious benefit of data

merger is improvement in spatial and temporal ocean color coverage. Data merger is the ultimate tool for 

the creation of ocean color climate data records. The major data merger effort undertaken by the SIMBIOS 

Project focused on integrating daily MODIS/Terra and SeaWiFS chlorophyll data sets. MODIS and 

SeaWiFS data were used to study methodologies to create a consistent series of long-term observations 

from sensors of different design, characterization, processing algorithms, and calibrations. Analyses of 

MODIS/Terra (Kilpatrick et al., 2002; Fargion and McClain, 2003) daily oceans data in comparison with 

SeaWiFS were performed to facilitate the merger efforts (http://simbios.gsfc.nasa.gov/~ewa/SeaMODIS/ 

seamodis-match.html). The analyses focused on assessing temporal trends in data discrepancies between 

MODIS and SeaWiFS and artifacts present in MODIS data caused by the difficulties in accurately 

characterizing this complex sensor for features such as polarization sensitivity. A time series of daily waterleaving radiance and chlorophyll products, evenly spread over the three years of joint MODIS/Terra and 

SeaWiFS coverage, was used to study MODIS trends in departure from SeaWiFS data and MODIS scan 

angle and latitudinal dependencies. Corresponding investigations were performed on a three-month time 

series of provisional MODIS/Aqua data and concurrent SeaWiFS coverage 

(http://simbios.gsfc.nasa.gov/~ewa/SeaMODISAqua/seamodis-aqua.html). The analyses provided vital 

information on MODIS data dependencies in relation to SeaWiFS ocean color records for the data merger 

activity. The information derived from MODIS/Terra and SeaWiFS comparisons was used to derive an 

ocean color sensor cross-calibration strategy where sensor artifacts, temporal, and spatial variabilities are 

not easily quantifiable and many dependencies are involved. Machine learning techniques were developed 

to cross-calibrate MODIS/Terra and SeaWiFS and produce a consistent ocean color baseline data set. The 

cross-calibration enabled the production of combined MODIS and SeaWiFS daily global chlorophyll

coverages which were free from temporal trends, as well as MODIS scan angle and latitudinal 

dependencies (Kwiatkowska, 2003). An example of the cross-calibration result is shown in Figure 1.5. 

Figure 1.5: Result of machine learning cross-calibration of MODIS and SeaWiFS data. A merged, 

consistent daily chlorophyll map was produced using original SeaWiFS chlorophyll and MODIS data

regressed to SeaWiFS baseline chlorophyll representation. 

8 
SIMBIOS Project Annual Report 

Statistical objective analysis was investigated to spatially and temporally interpolate MODIS/Terra 

(cross-calibrated with SeaWiFS) and SeaWiFS data onto daily global ocean color maps (Kwiatkoska and 

Fargion, 2002; Kwiatkoska 2001). The objective of the interpolation was to merge ocean color data sets 

using individual sensor chlorophyll accuracies and produce error bars for each data point. An addition to 

conventional statistical objective analysis was proposed to perform both space and time interpolation of 

ocean color data. Furthermore, the ensemble spatial and temporal correlation structure of the chlorophyll 

field was made dependent on the ocean spatial variability defined by local dynamical processes. An effort 

was made to merge ocean color data of different spatial resolutions to support data merger applications 

focused on local area coverage, such as on coastal zones. A wavelet transform multi-resolution analysis 

was applied to overlapping MOS and SeaWiFS scenes, where MOS data were at 0.5km resolution and 

SeaWiFS at 1.1km resolution (Kwiatkoska and Fargion, 2002a & b; Kwiatkoska 2001). The approach 

enabled enhancement of oceanic features in lower resolution imagery using higher resolution data. 

In addition, the SIMBIOS Project developed an operational merged product at the level of observed 

radiances, water-leaving radiances, or derived products such as chlorophyll. As a demonstration of this

technique, software and procedures were developed within the SIMBIOS Project to generate merged Level3 products from SeaWiFS and MODIS. In coordination with MODIS/Terra oceans collection #4 

reprocessing, the SIMBIOS Project began to receive daily Level-3 binned chlorophyll products, and to 

merge the MODIS products with SeaWiFS Level-3 chlorophyll products within the framework of the 

SeaWiFS Data Processing System (SDPS) (Figure 1.6). When the first daily binned chlorophyll products

from MODIS/Aqua became available, these were immediately incorporated into the merging process as 

well. 

Figure 1.6: MODIS 4.6 km product converted to 9-km SeaWiFS format merged with standard SeaWiFS 9-

km Level-3 chlor_a bin product, then mapped. 

The SeaWiFS products used in the merging are standard 9-km resolution bin files composited over 

one-day periods. The MODIS products are standard Level-3 daily binned files at 4.6-km resolution. The 

specific ocean color parameters used are the chlor_a product of SeaWiFS, which is the chlorophyll 

concentration derived using the OC4V4 algorithm (O’Reilly, 2000), and the chlor_a_2 product of MODIS 

(Terra & Aqua), which is the chlorophyll concentration derived with the OC3M algorithm (O’Reilly, 

2000). The MODIS product suite includes multiple chlorophyll products, but the chlor_a_2 product is 

considered to be the SeaWiFS-analog. Both the MODIS and SeaWiFS bin file formats use a sinusoidal

distribution of equal area bin elements; however, the MODIS products are generated at a higher resolution 

9 
SIMBIOS Project Annual Report 

than SeaWiFS. The first step in the merging process is to convert the MODIS products to the 9-km

resolution of SeaWiFS, to achieve a 1-to-1 mapping of the MODIS and SeaWiFS bins. The SIMBIOS 

Project developed two pieces of software to accomplish this task. The first, modbin2seabin, converts the 

MODIS format to a SeaWiFS-like format at the original MODIS bin resolution. This is just a slight

reorganization of the Hierarchial Data Format (HDF) fields. The second program, reduce_bin_resolution, is

essentially a modified version of the SeaWiFS time binning code, which performs spatial compositing of 

the input bins. For the MODIS files, reduce_bin_resolution effectively averages four 4.6-km bins into a 

single 9-km bin. The averaging is weighted by the square root of the number of observations within each 

4.6-km bin, which is the same approach used for standard temporal compositing of MODIS and SeaWiFS 

(Campbell et al., 1995). 

Once the MODIS products have been converted to SeaWiFS-like format and resolution, the standard 

SeaWiFS temporal binning code, timebin, is employed to composite the files from both missions into daily, 

weekly, and monthly Level-3 bin products at 9-km resolution. Again, the time binner performs a weighted 

average, with weights computed as the square root of the number of observations within each input bin. 

These binned products are then mapped using the standard SeaWiFS mapping software, smigen, and the 

mapped files and browse images are distributed through the SeaWiFS Standard Mapped Image browser at

http://seawifs.gsfc.nasa.gov/cgi/level3.pl?DAY=13Jul2002&PER=&TYP=modsea . 

1.5VALIDATION OF BIO-OPTICAL PROPERTIES 

A standard set of measurement protocols is indispensable in developing consistency across the variety

of international satellite ocean color missions either recently launched or scheduled for launch in the next 

few years. The SeaWiFS and SIMBIOS Projects allocated resources to describe and develop protocols or 

scientific approaches in accordance with the goals of the Projects (Mueller & Fargion, 2003; Fargion et al., 

2001). These efforts, described in NASA TMs, are intended to provide standards, which if followed 

carefully and documented appropriately, will ensure that any particular set of optical measurements will be

acceptable for ocean color sensor validation and algorithm development. The protocols are guidelines and 

may be somewhat conservative. Continued development and refinement of these protocols help ensure 

coordination, collaboration, and communication between those involved. Furthermore, calibration roundrobin intercomparison experiments are conducted by the Project (Riley and Bailey, 1998; Meister et al.,

2002 and 2003). 

The SIMBIOS and SeaWiFS Projects maintained a local repository of in situ bio-optical data, known 

as SeaBASS, to support and sustain their regular scientific analyses (Hooker et al., 1994; Werdell and 

Bailey, 2002; Werdell et al., 2003). This system was originally populated with radiometric and 

phytoplankton pigment data used in the SeaWiFS Project’s satellite validation and algorithm development 

activities. To facilitate the assembly of a global data set, SeaBASS was broadened to include

oceanographic and atmospheric data sets collected by the SIMBIOS Project, which aided considerably in

minimizing spatial and temporal biases in the data while maximizing acquisition rates. To develop 

consistency across multiple data contributors and institutions, the SIMBIOS Project also defined and

documented a series of in situ sampling strategies and data requirements that ensure that any particular set

of measurements are appropriate for algorithm development and ocean color sensor validation (McClain et 

al. 1992). The SeaBASS bio-optical data set includes measurements of apparent and inherent optical

properties, phytoplankton pigment concentrations, and other related oceanographic and atmospheric data, 

such as water temperature, salinity, and aerosol optical thickness (AOT). Data were collected using a 

number of instrument packages from a variety of manufacturers, such as profilers and handheld

instruments, on a variety of platforms, including ships and moorings. As of April 2003, SeaBASS included 

data collected by research groups at 44 institutions in 14 countries, encompassing over 1,150 individual 

field campaigns, including major international field experiments, such as the Asian Pacific Regional

Aerosol Characterization Experiment (ACE-Asia) and the Indian Ocean Experiment (INDOEX) (Figure 

1.7). 

The full data set includes over 300,000 phytoplankton pigment concentrations, 13,500 continuous 

depth profiles, 15,000 spectrophotometric scans, and 15,000 discrete measurements of AOT. Participants

of the SIMBIOS Program contributed just over 87% of these data. The SIMBIOS Project Office made use 

of a rigorous series of submission protocols and quality control metrics that range from file format 

verification to inspection of the geophysical data values (Fargion et al. 2001; Knobelspiesse et al., 2003;

10 
SIMBIOS Project Annual Report 

Mueller & Fargion, 2003). This ensures that observations fall within expected ranges and do not exhibit

any obvious characteristics of measurement problems. A consistent methodology for validating satellite 

data retrievals was developed and applied to OCTS, MOS, POLDER, SeaWiFS and MODIS providing a 

means of objectively analyzing validation results across missions by minimizing the effect of processing 

differences on the overall results. Briefly, the validation analysis requires coincident measured in situ and 

satellite observations, quality controlled data sets (both satellite and in situ), derived from a well-defined, 

objective set of exclusion criteria (McClain et al., 2000). An example of these validation results for 

SeaWiFS and MODIS (Terra) are presented in Figure 1.8. 

Figure 1.7: The global distribution of data included in the full SeaBASS bio-optical data set, as of April

2003. Clockwise from left: all archived data, chlorophyll a concentrations only (CHL), apparent optical

properties only (AOP), and aerosol optical thickness only (AOT). 

Figure 1.8: Scatter plots of coincident in situ and SeaWiFS (blue) and MODIS Terra (green) observations 

for chlorophyll a, water-leaving radiance (Lwn) at 443 nm, and aerosol optical thickness (τ) at 865 nm. The 

chlorophyll a data were transformed to account for their log-normal distribution. A one-to-one line has 

been included for clarity. 

11 
SIMBIOS Project Annual Report 

The SeaBASS World Wide Web site, located at http://seabass.gsfc.nasa.gov, provides a complete 

description of the system architecture, comprehensive documentation on policies and protocols, and direct

access to the bio-optical data set and validation results. Briefly, the architecture consists of geophysical 

data and metadata recorded in digital ASCII text files, which reside on a dedicated server at NASA GSFC, 

and a relational database management system (RDBMS) used to catalog and distribute the data and files. 

Through the use of online search engines that interface with the RDBMS, the full bio-optical data set is 

queriable and available to authorized users via the Web (Figure 1.9). To protect the publication rights of 

contributors, access to data collected more recently than January 1, 2000 is limited to SIMBIOS Science 

Team members, NASA-funded researchers, and regular voluntary contributors, as defined by the SeaBASS 

access policy (Firestone and Hooker, 2001). The remainder of the data is fully available to the public and, 

additionally, has been released to the National Oceanic and Atmospheric Administration’s (NOAA) 

National Oceanographic Data Center (NODC) for inclusion in their archive. As of September 2003, 45 

research groups outside of the SIMBIOS and SeaWiFS Project Offices have been granted unrestricted 

access to SeaBASS. In 2002, these groups queried SeaBASS over 950 times and downloaded more than 

60,000 data files from the bio-optical data set. During the same period, 146 research groups searched the 

public set 600 times and downloaded over 37,000 files. 

Figure 1.9: The full bio-optical data set is available online, using a variety of search engines and utilities. 

1.6 SUPPORT SERVICES 

In an effort to improve the quality and quantity of calibration and validation data sets, the SIMBIOS 

Project offered several support services to field investigators and the larger ocean color community. As of 

September 1, 2003, 397 cruises have been supported through services provided by the Project. These 

support services include: scheduling of on-board Local Area Coverage (LAC) recording for SeaWiFS; over 

12 
SIMBIOS Project Annual Report 

flight predictions for operational sensors; near real time SeaWiFS imagery for cruise locations; satellite 

data distribution and software support. The Project also provided optical instrumentation from a pool of 

investigator and project owned instruments; and round robin activities. Detailed information on the services 

is available on the SIMBIOS web site (http://simbios.gsfc.nasa.gov/). 

Scheduling SeaWiFS On-board LAC Recording 

 

Since much of the world’s oceans are not covered by a SeaWiFS High Resolution Picture 

Transmission (HRPT) station, high-resolution data may have been recorded onboard the SeaWiFS sensor. 

As a service to the science community, the SIMBIOS Project, in conjunction with the SeaWiFS Project, 

scheduled SeaWiFS onboard LAC for cruises that occurred 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 was allotted for each LAC target, which corresponded to 180 scan lines or approximately 200 km

along track at nadir. 

Overflight Predictions for Operational Sensors 

For calibration and validation purposes, in situ measurements should be made as close to the sensor 

over-flight time as possible. To aid investigators in determining when sampling should occur, the 

SIMBIOS Project offered over-flight predictions for all operational ocean color remote sensors. The 

sensors supported were SeaWiFS, MOS, Ocean Color Imager (OCI), OSMI, MERIS and MODIS (Terra & 

Aqua). 

Near Real Time SeaWiFS & MODIS Imagery 

In addition to providing predictions for satellite over-flight times, the SIMBIOS Project offered near 

real time imagery of the operational SeaWiFS products and MODIS (Terra & Aqua) to cruises at sea in

JPEG and png format. These images may provide useful information in cruise planning both prior to and 

during oceanographic cruises. Level-1 images are available for SeaWiFS, and Level-2 products for 

SeaWiFS and MODIS. The SeaWiFS default specifications for the images provided include: available 

LAC, HRPT, and Global Area Coverage (GAC) (MODIS will be full resolution LAC equivalent); true

color images from Level 1 bands 1, 5, and 6 (SeaWiFS only, no true color option available for MODIS);

chlorophyll-a (chlor_a_2 algorithm for MODIS); image width 600 pixels; minimum percent valid

chlorophyll pixels: 5%; same days as LAC coverage (SeaWiFS only). Images are also customized to best

accommodate individual investigator needs. 

Satellite Data Distribution

The OCTS is an optical radiometer which flew on the Japanese Advanced Earth Observing Satellite 

(ADEOS) from August 1996 to June 1997, collecting 10-months of global ocean color data. During the 

ADEOS mission lifetime, approximately 450 GB of real-time, 700m-resolution OCTS data were collected 

by the SeaWiFS project through NOAA ground stations at Wallops, Virginia and Fairbanks, Alaska. The 

archive consists of 337 scenes of the U. S. East Coast and 1,311 scenes over Alaska. These data were 

processed from raw telemetry through level-2 ocean color products using software developed by SIMBIOS 

and products distributed through a browse utility linked to the SIMBIOS Project's web site. Furthermore, 

the entire OCTS GAC data was reprocessed by the Project in 2001. This was a very productive 

collaboration with NASDA and Japanese scientists. OCTS-GAC data is available through the GSFC

DAAC, the SIMBIOS Project and NASDA. Descriptions of the data processing stream, OCTS-specific 

modification to the algorithms, and statistical comparisons between OCTS and SeaWiFS can be found at:

http://seawifs.gsfc.nasa.gov/SEAWIFS/RECAL/OCTS_Repro1/.

Since February 1999, the project has been operating a receiving station at NASA's Wallops Flight 

Facility (WFF) to acquire data from 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 the German 

13 
SIMBIOS Project Annual Report 

Remote Sensing Data Centre (DLR-DFD) for archive and distribution. In addition, the SIMBIOS Project

processes the data through level-1B using standard software provided by the German Institute for Space 

Sensor Technology (DLR-ISST) (Neumann et al., 1995). All MOS data (1999-2003) processed by the 

SIMBIOS project is made available through the MOS browse system on the SIMBIOS web page. 

In coordination with MODIS-Terra Collection 4 reprocessing, the Project initiated an operational 

process to collect and merge MODIS daily global chlorophyll products with SeaWiFS daily products. The 

merging scheme is a simple weighted averaging using standard SeaWiFS time-binning software. Presently

a complete set of daily, weekly, and monthly merged chlorophyll products, including various perturbations 

such as MODIS-Terra with MODIS-Aqua, MODIS-Terra with SeaWiFS, MODIS-Aqua with SeaWiFS, 

and MODIS-Terra/MODIS-Aqua/SeaWiFS are made available at the SIMBIOS web page 

(http://seawifs.gsfc.nasa.gov/cgi/level3.pl?DAY=05Mar2000&PER=&TYP=tmsea). The merging process 

is fully automated and operational, with new products generated as soon as the MODIS data became 

available. The merged products can be displayed and manipulated with standard SeaWiFS software tools 

such as SeaDAS. 

Diagnostic Data Set

During the first three SIMBIOS Science team meetings, it was recommended that a “diagnostic data

set” be created for each ocean color sensor to aid in comparing data products and to allow rapid 

reprocessing of selected areas for calibration and algorithm evaluation. Two conditions for the selection of 

a diagnostic data set site were formulated. First, a reliable source of in situ data (bio-optical and/or 

atmospheric) for the site had to exist, and second, the principal investigator had to be willing to share the in

situ data with the SIMBIOS project. Sites used as vicarious calibration sources were ranked with the 

highest priority. Time series sites were ranked as priority 2. All other sites were ranked as priority 3. 

Several sites were recommended, but did not meet one or both of the defined criteria. Several of the sites 

were modified, either at the request of an investigator, in order to reduce redundancy or improve coverage 

(by reducing the amount of land included in the extracted data). The list of sites as currently implemented 

is found in Table 1.4 and Figure 1.10 shows the MOBY diagnostic site. 

By midyear 2001, the SIMBIOS project, in conjunction with the SeaWiFS project, had begun 

production of the L1A and L2 subsets of SeaWiFS LAC resolution data for the list of diagnostic data set

sites. Prior to the start of the Collection 4 reprocessing of MODIS (Terra) data in March of 2002, the 

SIMBIOS project approached the MODIS Oceans Team with the request that MODIS produce a 

comparable set of extracted L1 and L2 data for inclusion in the diagnostic data set. The MODIS team

agreed. However, as a consequence of the flow of data through MODAPS (the MODIS Adaptive 

Processing System), MODIS Oceans provides L1B and L2 extracts, rather than L1A as was the 

recommendation of the SIMBIOS Science Team and IOCCG Working Group. The diagnostic data set files 

produced by MODAPS are sent to the SIMBIOS project for post processing. In order to ensure that only

useful data are included in the diagnostic data set, a threshold on the number of valid pixels within the 

region of interest was set. If this threshold (currently 25%) is not met, the files are excluded from further 

processing. If the threshold is met, a L2 (chlorophyll) browse image and two TAR files are created: one 

TAR file for the L2 granules and one for the L1B granules. Since the MODIS data are produced in five 

minute granules, the region of interest for a given site may cross the boundary between two granules. 

When this occurs, all L2 products from both granules are placed in the same TAR file, likewise for the L1B 

granules. The TAR files are then compressed using gzip compression. 

The SIMBIOS and MODIS teams worked with the Goddard Distributed Active Archive Center 

(GDAAC) to make the diagnostic data set files for SeaWiFS and MODIS (Terra and Aqua) available 

through the GDAAC. All the necessary mechanisms for the transfer of the dataset to the GDAAC have 

been put in place, the most critical of which was the creation of six new Earth Science Data Type (ESDT) 

definitions, one for each L1 and L2 data type as well as for the SeaWiFS, MODIS-Terra and MODIS-Aqua 

data sources. Once the data are archived at the GDAAC, they are visible to both the GDAAC WHOM

search engine and the EOSDIS EDG search engine. The diagnostic data set allows for the rapid processing 

and testing of atmospheric correction and geophysical product algorithms. The current list of sites cover a 

wide range of water types and aerosol conditions (see Table 1.4), which will aid algorithm assessments. 

14 
SIMBIOS Project Annual Report 

Figure 1.10: MOBY diagnostic site

SEADAS Software 

The SeaDAS is a comprehensive software package for processing, displaying and analyzing all

SeaWiFS data products (http://seadas.gsfc.nasa.gov). It was designed to serve a wide range of users, 

including individual scientists, SeaWiFS ground stations, and operational or commercial users. SeaDAS 

also provides level conversion processing designed to accurately replicate the operational data products 

(geophysical fields and data formats) generated by the SeaWiFS Project, when using the default input

values. In addition, SeaDAS allows for flexibility in the algorithms applied, the map projections used, and

other aspects of processing and analyses that enable users to customize their data products. Flexibility is 

further enhanced by providing executable programs, for those who only need the basic capabilities, and

source code, for those who wish to insert alternative algorithms. The SeaDAS software package contains a 

full suite of interactive display and basic analysis tools. The SeaDAS tool kit includes many navigation, 

display, analysis, and output functions. Navigation functions include data registration, map projections, 

overlaying of coastlines, plotting of in situ data, and latitude/longitude point location. General display 

functions include data scaling, color bar definition, annotation, zooming, roaming, and color palette

manipulation. General analysis functions include bathymetry generation, simple arithmetic functions, 

contour plots, profile plots, scatterplots, and histograms. Output functions allow output of either data or 

latitude/longitude values (ASCII, HDF, and binary flat files formats) or displayed images (PNG and 

PostScript formats). The SeaDAS group is co-located with the SeaWiFS and SIMBIOS Projects. SeaDAS 

was augmented by the SIMBIOS Project to include OCTS, MOS, OSMI, and soon, MODIS. The current

SeaDAS user community includes approximately 500 research sites in 45 countries. 

15 
SIMBIOS Project Annual Report 

Table 1.4 Diagnostic site ID and location 

Site ID Location North

latitude

South 

latitude

West

longitude 

East 

longitude 

Contact PI 

OCAlbron Alberon Gyre

Eastern Med. 

33.5N 32.5N 32.0 E 33.0E

OCA Line Japan East coast 42.0 N 41.0N 145.283E 146.283E Tsuda 

OCAfrica Mauritanian

Upwelling 

21.5N 20N 18W 17W Carder

OCBahrain Bahrain, Persian

Gulf 

26.816 N 25.816N 50.0 E 51.0E 

OCBATS BATS Bermuda 33.0N 31.0N 65.5W 63.5W Nelson

OCCALCO

F 

CALCOFI, 

California Coast 

34.5N 30.5N 124.0W 122.0W Mitchell

OCCpVerd Capo Verde, NW

African Coast 

17.217N 16.217N 23.433W 22.433W Carder

OCCariac Cariaco Basin,

Venezuela 

11.0N 10.0N 65.66W 64.16W MuellerKarger 

OCChsBay Chesapeake Bay 39.5N 36.8N 76.8W 75.6W Harding

OCCook Cook Island,

Western South 

Pacific 

19.5S 20.5S 163.5W 162.5W

OCDryTrt Dry Tortugas,

Florida Keys 

21.1N 24.1N 83.283W 82.283W Voss

OCEaster Easter Island,

South Pacific Gyre 

26S 28S 116W 114W SIMBIOS

Project 

OCEqPAC Eastern Equatorial

Pacific 

0.5N 0.5S 155.5W 154.5W Chavez

OCFRONT Long Island, 

New York 

41.45N 40.45N 72.5W 71W Morrison

OCGlapgo Galapagos Islands 2.13N 1.87S 98.81W 88.81W Feldman

OCHattrs Cape Hatteras 37.5N 34.5N 76.5W 73.5W Stumpf & 

Cota 

OCHlglnd Helgoland,

North Sea 

54.6N 53.6N 7.3E 8.3E

OCHOT HOT Station,

Hawaii 

23.25N 22.25N 158.5W 157.5W Letelier

OCKshdoo Kaashidoo,

Maldives Islands

5.45N 4.45N 72.95E 73.95E Holben &

Frouin 

OCKNOT KNOT Station,

NW Pacific 

44.5N 43.5 N 154.5E 155.5E Saitoch

OCKorean Korean seawater

Monitoring site, 

East China Sea 

32.5N 31.5N 124.5E 125.5E Kim

OCLeo 15 LEO 15 Station, 

New Jersey

40.1 38.5N 74.75W 73.5W Arnone

OCLigurn Ligurian Sea,

Mediterranean

43.87 N 42.87 N 7.4 E 8.4 E Antoine 

OCLderz Luderitz

Upwelling, 

Namibian Coast 

25.5S 26.5S 14E 15E

OCMOBY MOBY Buoy,

Hawaii 

21.3N 20.3N 157.75 W 156.7W Clark & 

Trees 

OCMontry Monterey Bay, 37N 36.5N 122.75W 121.75W Chavez

16 
SIMBIOS Project Annual Report 

California 

OCNOAAG

M 

Northern Gulf of 

Mexico

30N 29N 88W 87W Arnone

OCNordic Baltic Sea 55.5N 54.5N 18.8E 19.8E

OCPAPA Station PAPA,

North Pacific 

52 N 48 N 147 W 143 W

OCPhlipp Philippines 17.5 N 16.5 N 132.5 E 133.5 E 

OCPlumes Plumes and

Blooms region, 

Santa Barbara, CA 

35.5 N 32.5 N 122 W 118 W Siegel

OCPlymbdy PlyMBoDY

Mooring, English 

Channel

50.4 N 49.6 N 4.8 W 3.4 W Aiken 

OCRttnst Rottnest Island,

Western Australia 

31.3 S 32.3 S 114.8 E 115.8 E Lynch 

OCSctian Scotia Prince Ferry 

Route, Gulf of 

Maine 

43.8N 42.8N 70.25W 65.75W Balch

OCTahoe Lake Tahoe 39.671N 38.671N 120.604W 119.604W

OCVenice Venice Tower

(AAOT) Northern 

Adriatic 

45.6N 44.8N 12.2E 13.4E Zibordi

OCWrmPol Warm Pool,

Western Equatorial

Pacific 

0.5N 0.5S 164.5E 165.5E

OCYBOM YBOM

replacement 

mooring, East

China Sea 

24.89N 23.89N 122.77E 123.77E Ishizaka

Instrument Pool 

Over the the years the Project has maintained and deployed a pool of sun photometers and above water 

radiometers intended to complement in-water optical measurements as well as the land-based Aerosol 

Robotic Network (AERONET) sun photometer network. The overall goal was to study aerosol optical

properties and validate satellite retrievals of aerosol optical properties. 

Three types of instruments composed the instrument pool (http://simbios.gsfc.nasa.gov/

Sunphotometers/). The first type was a sun/sky photometer that measured the solar irradiance and the sky

radiance. The second type was the shadow-band radiometer that measured the diffuse and total sky

radiance. The third type was the Micropulse Lidar (MPL), which measured the vertical and horizontal

distribution of aerosol backscatter, extinction and optical depth. The instruments were deployed by

SIMBIOS or NASA principal investigators on cruises and the collected data was archived in SeaBASS. 

This instrument pool included fourteen Microtops II sun photometers (Morys et al., 2001), one SIMBAD 

and two SIMBADA above water radiometers/sun photometers (Deschamps et al., 2003), 2 PREDE sun 

photometers and one micro-pulse Lidar. The description, characteristics and advantages of each instrument 

have been reviewed in project protocols and project annual reports. 

In addition to the instrument pool, the Project augmented the existing AERONET, which is dedicated 

to monitoring aerosol optical thickness (AOT) around the globe by supplying additional CIMELS. Most of 

the sun photometers used within the AERONET project (Holben et al., 1998) are in continental zones, and 

SIMBIOS enhanced this network with island and coastal stations (Figure 1.11). SIMBIOS CIMEL sites 

included Lanai Hawaii (with backup in Honolulu, USA), Ascension Island, Bahrain, Papeete (Tahiti), 

Wallops Island (USA), Anmyon Island and Chinae (South Korea), Erdemli (Turkey), Horta (The Azores), 

Puerto Madryn (Argentina), Dahkla (Morocco) and Rottnest Island (Australia). This deployment activity 

ended in 2001 and all instrument are now managed by the AERONET group. SIMBIOS coastal stations 

have been used by the atmospheric community and their data have been used in several scientific papers. 

17 
SIMBIOS Project Annual Report 

Figure 1.11: SIMBIOS CIMEL coastal and island sites. 

SIMBIOS Project has taken a number of steps to ensure the consistency and quality of this atmospheric 

data set. Calibration was performed approximately every three months. Details on the operation, calibration 

and theoretical principles of sun photometry are posted at http://simbios.gsfc.nasa.gov/Sunphotometers 

/principle.html. To aid the organization and dissemination of collected data, the sun photometer instrument

pool web site posts information about the deployment and data processing status of each instrument, along 

with calibration coefficient histories (Figure 1.12). 

Several steps were taken to ensure data quality consistency from several instrument designs (Figure 

1.13). The ACE-Asia cruise, on the R/V Ron Brown, was an ideal platform to validate SIMBIOS sun 

photometers. The R/V Ron Brown, which departed from Hawaii on March 15, 2001 and arrived in

Yokosuka, Japan on April 19, 2001, encountered a variety of aerosol types, from maritime low optical 

thickness conditions to extremely high optical thickness due to Asian dust. Visual inspections of data time 

series suggest that despite differences in instrument design, calibration and deployment, AOT and 

Ǻngstrom exponent typically agree within uncertainties. Data from instruments whose bands (1) have 

similar (within 10nm) center wavelengths, and (2) have calculated uncertainty values were analyzed to find 

measurements taken within fifteen minutes of each other. These temporally similar measurements were 

plotted to assess trends or biases between the data. Nearly all AOT and Ǻngstrom exponent data fall within

one uncertainty unit of the 1:1 line (data not shown). Generally speaking, at least 70% of all AOT data

compare within one uncertainty unit of the value from another instrument, with the best agreement between

the hand held instruments. With its high uncertainty values, the Angstrom exponent comparisons are even 

better, at 90% or more. Other problems, such as sun pointing for the Microtops II, were resolved by

modifying the measurement protocols(Knobelspiesse et al., 2003) and performing and uncertainty analysis

for each instrument. Uncertainty values were computed and archived for each AOT and Ǻngstrom

exponent measurement (Deschamps eta l.,2003; Russell et al., 1993; Miller et al., 2003). Computation of 

the Ǻngstrom exponent, which expresses the spectral character of the AOT values measured by sun 

18 
SIMBIOS Project Annual Report 

Figure 1.12: Instrument schedule for the pool deployment, calibration and data processing. 

photometers, was standardized using multiple bands and a linear fitting routine to paired wavelength and 

natural logarithm of AOT values. The Ǻngstrom exponent is the negative slope of this fit. This Ǻngstrom

exponent calculation method uses a recursive routine that makes an analytical computation of uncertainty

impossible. To account for this, an Angstrom exponent calculation method was devised that incorporates 

the individual AOT uncertainties and the Chi-square error to determine and Ǻngstrom exponent

19 
SIMBIOS Project Annual Report 

uncertainty. For several years the Project collected, processed and archived optical aerosol data from

handheld sun photometers in marine locations. After standardizing the data processing of MicroTops and 

SIMBAD/A by using identical calibration methods, ancillary data and processing software, a statistical 

analysis was done (Knoblespiesse et al. 2003b). Statistical analyses reveals a dataset influenced by its

temporal and geographical distribution, while the multi-modal histogram for AOT and Ǻngstrom exponent

reveal varied aerosol populations (Knoblespiesse et al. 2003). This separation was validated by showing 

individual classes more likely to be log-normally (for AOTs) or normally (for Ǻngstrom exponents) 

distributed than the dataset as a whole. Properties of each class are represented in Figure 1.14. Results are 

also compared with the SeaWiFS atmospheric correction-aerosol models (Figure 1.15). The implications of 

this comparison are discussed in Knoblespiesse et al (2003b) and in Chapter 19 by M. Wang. 

Figure 11.3: Validation of the SIMBIOS sun photometers on the ACE-Asia cruise. 

Figure 1.14: Location on an AOT/Angstrom Exponent scatter plot of each class. Data in blue represent 

class 1 type aerosols, encountered in 'maritime' conditions, while orange represents class 2 'dust', green 

shows class 3 aerosols and red represents class 4 aerosols encountered in 'urban' conditions. Data in black 

could not be classified.

20 
SIMBIOS Project Annual Report 

Figure 1.15: Histograms of AOT at 500nm and Angstrom Exponent for classified aerosol data. Class 1 is

presented in blue, class 2 in orange, class 3 in green and class 4 in red. Bin sizes used in histogram

computation were 0.02 for AOT at 500nm (A) and 0.1 for Angstrom Exponent (B). Vertical black bars on 

the Angstrom Exponent histogram show the equivalent Angstrom Exponents for SeaWiFS aerosol models

(Gordon and Wang, 1994), (Shettle and Fenn, 1979).

21 
SIMBIOS Project Annual Report 

1.7 CALIBRATION ROUND ROBIN 

Two kinds of activities were performed by the Project to further ensure the adequate quality of in situ

data. First, as mentioned, measurement protocols were developed, and their usage by the science 

community encouraged. Second, calibration round robin intercomparison experiments were conducted. 

The first SeaWiFS Transfer Radiometer (SXR) was built for the SeaWiFS Project to verify and compare 

measurements of spectral radiance at six discrete wavelengths in the visible and near infrared (Johnson et 

al., 1998). The SXR is currently used to compare these sources to standards of spectral radiance 

maintained at the National Institute of Standards and Technology (NIST). The SIMBIOS Project had a 

second copy of the SeaWiFS Transfer Radiometer (SXR-II) built for use in the calibration round robin. 

This unit supplemented the first unit and was designed for easier travel. NASA personnel executed the first

SIMBIOS-sponsored calibration round-robin experiment (the sixth SeaWiFS Intercalibration Round-Robin 

Experiment or SIRREX-6; previous round-robin had been sporsored by the SeaWiFS Project) from August

1997 to February 1998 (Riley and Bailey, 1998). 

In SIRREX-6, four common field instruments (Satlantic in-water radiometers) were taken to nine 

separate laboratories and tested using the laboratories’ standards and procedures. Two of the sensors were 

seven-channel radiance heads and two were seven-channel irradiance heads. The calibration and data 

reductions procedures used at each site followed the laboratories’ normal procedures. The reference lamps 

normally used for the calibration of these types of instruments by the various laboratories were also used 

for this experiment. Project personnel processed the data to produce calibration parameters from the 

various laboratories for comparison. These tests showed an overall agreement at better than the +/-2% 

level. The SIRREX-6 was followed by the SIMBIOS Radiometric Intercomparison experiments (SIMRIC-I 

and II) in 2001 and 2002 (Fargion and McClain, 2003; Meister et al., 2002; Meister et al. 2003). The 

purpose of these round-robins was to: 1) verify that all laboratories were on the same radiometric scale; 2) 

detect and correct problems at any individual laboratory in a timely fashion; 3) encourage the common use 

of calibration protocols; 4) identify areas where the calibration protocols need to be improved; and 5) 

document the calibration procedures specific to each laboratory. The participating laboratories included 

academic institutions, government agencies and instrument manufacturers that either directly or indirectly 

contributed to SeaBASS. They were, in alphabetical order: Biospherical Instruments Inc.; HOBI Labs Inc.;

ICESS at the University of California, Santa Barbara; MOBY Project of Moss Landing Marine 

Laboratories; NASA Code 920.1, Goddard Space Flight Center; NASA Code 972, Wallops Flight Facility; 

Naval Research Laboratories, Washington; Satlantic Inc., Canada; Scripps Institute of Oceanography, 

University of California, San Diego; University of Miami; University of South Florida, St. Petersburg. 

The SXR-II was calibrated on a yearly basis at NIST, in the Spectral Irradiance and Radiance 

Calibration with Uniform Sources (SIRCUS) facility. The radiometric stability of the SXR-II between 

NIST calibrations was monitored by the portable light sources the SeaWiFS Quality Monitors (SQM), the 

OCS-5002 from YES, Inc., and the SQM-II from Satlantic, Inc. The radiances produced by the laboratories 

for calibration were measured in the six SXR-II channels from 411 nm to 777 nm and compared to the 

radiances expected by the laboratories. Typically, the SXR-II measured radiances differed from the 

radiances expected by the laboratories by less than 2%. This level of agreement is satisfactory. In some

cases, larger deviations were found and tracked to issues such as improper baffling, incorrect setup of the 

light sources, or deterioration of the main calibration bulb of the respective laboratory. Several issues were 

identified where the calibration protocols needed to be improved, especially the reflectance calibration of 

the reference plaques and the distance correction when using the irradiance standards at distances greater 

than 50 cm. 

1.8 CONCLUDING REMARKS 

Past and present satellite missions typically provide observations that are limited to the short-term, to

specific processes of scientific interest or to test new technologies. Production of long-term products

requires cross-calibrated measurements that have been merged in space and time measurements. The 

production of these products is usually beyond the scope of individual missions. SIMBIOS Program goals

and tasks addressed the complexity of issues involved with how to produce a long-term calibrated data set

across missions for use in climate research. 

22 
SIMBIOS Project Annual Report 

The specific objectives of the SIMBIOS Program were: (1) to quantify the relative accuracy of 

measurements from the ocean color products from each mission, (2) to work with each project to improve 

the level of confidence and compatibility among these products, and (3) to develop methodologies for 

generating merged level-3 products. Clearly, these objectives encompass both experimental and operational

requirements. The Project has established links with all of currently operational missions (1997-2003) and 

has built up an operational project with the activities described in Figure 1.1. These activities build on the 

“know how” and experience of the SIMBIOS and SeaWiFS inhouse staff. One of the SIMBIOS Project’s 

unique capabilities is to address the complexity of all the characterization and calibration processing steps 

as a “start to finish” process. This approach is a success story on how to tackle these issues while engaging 

the ocean color community. An open, peer review process within the Project staff and with the larger ocean 

color community on algorithm development, merging techniques and protocol development is the valid

model for the operation of future missions. 

The following specific conclusions may be drawn from the SIMBIOS experience: simple merging 

techniques (big bin or other averaging methods) applied on known biases result in degradation of merged 

data set and is not recommended; cross calibration and characterization in time and space with overlapping 

periods are essential steps before proceding with data set merging or producing long time series; neural

network and semi-analytical merging algorithms can produce merged products with lower uncertainties 

while providing the desired improvements in daily coverage, and both methods require a priori knowledge 

of the uncertainty and charaterization over time of the merged missions; the funded global in situ biooptical collection and SeaBASS data set have been invaluable resource for evaluating satellite products and 

data merging methodologies and it, or a similar data set, is required for further efforts. 

The achievements were certainly due to the well thought out tasks (accurate characterization, 

calibration, establishment of traceability by national measurement institutions, validation, etc.) and 

operational configuration (Figure 1.1). Over the past several years, a set of key resources were developed: 

• a comprehensive in situ bio-optical database; 

• a program to evaluate different atmospheric correction algorithms; 

• a program to link the calibrations of individual ocean color satellite instruments; 

• a program (including cross-calibrations and measurement protocols) to develop a consistent in situ

calibration and validation data set for the satellite measurements; 

• a model for funded collection of in situ data, including rapid turn around;

• alternate algorithms to convert radiometric measurements to derived geophysical products; 

• alternate methods to combine ocean color measurements from different sources into a single data 

set; and

• a strong documentation record. 

The calibration and validation programs for individual missions (both domestic and international) had 

a wide range of approaches and methodologies, making international cooperation imperative to ensure high 

quality climate data. Calibration of long-term, high quality data, mission data overlap and consistent data 

sets are the basis of data stability. Natural signals are very small and impossible to detect with unstable 

and/or gappy data. Further data stability requires a system that should have the capability to reprocess large 

data sets as our understanding improves. Our hope is that the organizational structure, lessons learned, and 

knowledge achieved by SIMBIOS will benefit future ocean color programs. Finally, and perhaps most 

importantly, the SIMBIOS program demonstrated that international projects, agencies, and science teams 

could work effectively together in tangible ways (e.g., sharing data, processing code, algorithms, and 

product evaluations, and in collaborating on joint mission reprocessings) to improve the products each 

mission was generating and to generate a high quality time series of global observations. 

REFERENCES 

Baith, K., R. Lindsay, G. Fu, and C.R. McClain, 2001: SeaDAS: Data Analysis System Developed for 

Ocean Color Satellite Sensors. Eos Trans. AGU, 82(18), 202. 

23 
SIMBIOS Project Annual Report 

Campbell, J. W., J. M. Blaisdell, and M. Darzi, 1995: Level-3 SeaWiFS Data Products: Spatial and 

Temporal Binning Algorithms, NASA Technical Memorandum 104566, Volume 32, S.B. Hooker, E.R.

Firestone, and J.G. Acker, Eds., NASA Goddard Space Flight Center, Greenbelt, Maryland. 

Deschamps, P-Y, B. Fougnie, R. Frouin, P. Lecomte and C. Verwaerde, 2003: SIMBAD: A field 

radiometer for satellite ocean-color validation, Applied Optics (in press). 

Dubovik, O., et al., 2000: Accuracy Assessments Of Aerosol Optical Properties Retrieved From Aerosol

Robotic Network (AERONET) Sun And Sky Radiance Measurements, J. Geophys. Res., 105, D8, 

9791-9806. 

Fargion G.S. and C.R. McClain, 2001: SIMBIOS Project 2000 Annual Report, NASA Tech. Memo. 2000-

209976, NASA GSFC, Greenbelt, Maryland, 164 pp., http://simbios.gsfc.nasa.gov/ye_reports.html.

Fargion, G. S. et al., 2001: In situ Aerosol Optical Thickness Collected by the SIMBIOS Program (1997-

2000): Protocols, and Data QC and Analysis, NASA Tech. Memo. 2001-209982, NASA GSFC, 

Greenbelt, Maryland, 103 pp., http://simbios.gsfc.nasa.gov/ye_reports.html. 

Fargion G.S. and C.R. McClain, 2002: SIMBIOS Project 2001 Annual Report, NASA Tech. Memo. 2002-

210005, NASA GSFC, Greenbelt, Maryland, 184 pp., http://simbios.gsfc.nasa.gov/ye_reports.html.

Fargion G.S. and C.R. McClain, 2003a: SIMBIOS Project 2002 Annual Report, NASA Tech. Memo. 2003-

211622, NASA GSFC, Greenbelt, Maryland, 157 pp., http://simbios.gsfc.nasa.gov/ye_reports.html.

Fargion G.S. et al., 2003b: MODIS, Validation, Data Merger and Other Activities Accomplished by the 

SIMBIOS Project: 2002-2003, NASA Tech. Memo., Goddard Space Flight Center, Greenbelt , 

Maryland, 1-80 (in press). 

Firestone, E.R., and S.B. Hooker, 2001: Seawifs Postlaunch Technical Report Series Cumulative Index:

Volumes 1-11, NASA Tech. Memo. 2001-206892, Vol. 12, S.B. Hooker and E.R. Firestone, Eds., 

NASA GSFC, Greenbelt, Maryland, 24 pp. 

Franz, B.A., et al., 2001: A Three-Year Intercomparison of Oceanic Optical Properties from MOS and 

SeaWiFS, Proc. AGU 2001, Fall Meeting, San Francisco, California. 

Franz, B.A., and Y. Kim, 2001: A Comparative Study and Intercalibration between OSMI and SeaWiFS, 

Proc. AGU 2001, Fall Meeting, San Francisco, California. 

Franz, B.A., et al., 2001: SeaWiFS Vicarious Calibration: An Alternative Approach Utilizing Simultaneous 

In situ Observations of Oceanic and Atmospheric Optical Properties, NASA Tech. Memo.2001-209982, 

137 pp., 2001. 

Gordon H.R. and M. Wang, 1994: Retrieval of Water-Leaving Radiance and Aerosol Optical Thickness 

Over the Oceans With Seawifs: A Preliminary Algorithm, Appl. Opt., 33, 443-452. 

Holben B.N., T.F. Eck, I. Slutsker, et al., 1998: AERONET - A federated instrument network and data

archive for aerosol characterization”, Remote Sens. Environ., 66, 1-16, 1998. 

Hooker, S.B. et al., 1994: The SeaWiFS Bio-Optical Archive And Storage System (SeaBASS), Part 1, 

NASA Tech. Memo. 104566, Vol. 20, S.B. Hooker and E.R. Firestone, Eds., NASA GSFC, Greenbelt,

Maryland, 37 pp.. 

Johnson B.C., Fowler J.B., Cromer C.L., 1998: The SeaWiFS Transfer Radiometer (SXR), NASA

Tech. Memo. 1998-206892, Vol. 1, NASA GSFC, Greenbelt, Md., 58 pp. 

24 
SIMBIOS Project Annual Report 

Kilpatrick, K., et al., 2002: Time Series of Calibrated Ocean Products from NASA’s Moderate Resolution 

Scanning Spectrometer (MODIS), Proceedings of the Ocean Sciences Meeting, Honolulu, Hawaii.

Knobelspiesse, K.D., C. Pietras and G.S. Fargion, 2003: Sun-Pointing Error Correction for Sea Deployment

of the Microtops II Handheld Sun Photometer, J. Atmos. Ocean. Tech., Vol. 20, No. 5, 767-771. 

Knobelspiesse, K.D., C. Pietras, G.S. Fargion, M. Wang, R. Frouin, M.A. Miller, A. Subramaniam and 

W.M. Balch, 2003b: Maritime aerosol optical properties measured by handheld sun photometers. 

Submitted to Remote Sensing of Environment 

Kwiatkowska, E.J., 2003: Application of Machine Learning Towards the Creation of a Consistent and 

Calibrated Global Chlorophyll-a Concentration Baseline Data Set, IEEE Trans. on Geoscience and 

Remote Sensing, (accepted). 

Kwiatkowska E.J. and G.S. Fargion, 2002: Merger of Ocean Color Information from Multiple Satellite 

Missions under the NASA SIMBIOS Project Office, Proc. of the Fifth International Conference on 

Information Fusion, Annapolis, Maryland, 1, 291-298. 

Kwiatkowska E.J. and G.S. Fargion, 2002: Merger of Ocean Color Data from Multiple Satellite Missions 

within the SIMBIOS Project”, Proc. of SPIE Symposium, Hangzhou, China. 

Kwiatkowska-Ainsworth, E.J, 2001: Merger of Ocean Color Information of Different Spatial Resolution: 

SeaWiFS and MOS”, EOS Trans. American Geophysical Union, Vol. 82, no. 47, 675. 

McClain, C. R., W. E. Esaias, W. Barnes, B. Guenther, D. Endres, S. B. Hooker, B. G. Mitchell and R.

Barnes, 1992: SeaWiFS Calibration and Validation Plan, NASA Tech. Memo. 104566, Vol. 3, S. B. 

Hooker and E. R. Firestone (eds.), NASA Goddard Space Flight Center, Greenbelt, Maryland, 41 pp.,. 

McClain, C.R., et al., 2002: The Proposal for the NASA SIMBIOS Program, 1995, NASA Tech. Memo. 

2002-210008, NASA GSFC, Greenbelt, Maryland, 54 pp., 

http://simbios.gsfc.nasa.gov/ye_reports.html.

McClain C.R. and G.S. Fargion, 1999: SIMBIOS Project 1998 Annual Report, NASA Tech. Memo. 1999-

208645, NASAGSFC, Greenbelt, Maryland, 105 pp., http://simbios.gsfc.nasa.gov/ye_reports.html.

McClain C.R. and G.S. Fargion, 1999: SIMBIOS Project 1999 Annual Report, NASA Tech. Memo. 1999-

209486, NASA GSFC, Greenbelt, Maryland, 128 pp., http://simbios.gsfc.nasa.gov/ye_reports.html.

McClain C.R., R.A. Barnes et al., 2000: SeaWiFS Postlaunch Calibration and Validation Analyses, Part 2, 

NASA Tech., Memo. 2000-206892, Vol 10, S.B. Hooker and E.R. Firstone, Eds., NASA GSFC, 

Greenbelt, Maryland, 57pp. 

Meister G. et al., 2002: The First SIMBIOS Radiometric Intercomparison (SIMRIC-1), April-September 

2001, NASA Tech. Memo. 2002-210006, GSFC, Greenbelt, Maryland, 60 pp., 

http://simbios.gsfc.nasa.gov/ye_reports.html.

Meister G. et al., 2003: Comparison of Spectral Radiance Calibrations at Oceanographic and Atmospheric 

Research Laboratories”, Metrologia, Vol. 40, 93-96. 

Miller, M.A., M.J. Bartholomew, and R.M. Reynolds, 2003: The Accuracy of Marine Shadow-band Sun 

Photometer Measurements of Aerosol Optical Thickness and Angstrom Exponent”, J. Atmos. Ocean 

Tech (in press). 

Morys, M., F. M. Mims III et al., 2001: Design, Calibration, And Performance Of MICROTOPS II 

Handheld Ozone Monitor And Sun Photometer, J. Geophys. Res., 106, 14, 573-14,582. 

25 
SIMBIOS Project Annual Report 

Mueller J.M. and G. S. Fargion, 2003: Ocean Optics Protocols for Satellite Ocean Color Sensor Validation, 

Revision 4, Part I, NASA Tech. Memo. 2003-210004/Rev 4/Vol.I, NASA Goddard Space Flight Center, 

Greenbelt, Maryland, 1-50 pp. http://simbios.gsfc.nasa.gov/ye_reports.html. 

Mueller J.M. and G. S. Fargion, 2003: Ocean Optics Protocols for Satellite Ocean Color Sensor Validation, 

Revision 4, Part II, NASA Tech. Memo. 2003-210004/Rev 4/Vol. II, NASA Goddard Space Flight 

Center, Greenbelt, Maryland, 1-56 pp. http://simbios.gsfc.nasa.gov/ye_reports.html. 

Mueller J.M. and G. S. Fargion, 2003: Ocean Optics Protocols for Satellite Ocean Color Sensor Validation, 

Revision 4, Part III, NASA Tech. Memo. 2003-210004/Rev 4./Vol.III, NASA Goddard Space Flight 

Center, Greenbelt, Maryland, 1-78 pp. http://simbios.gsfc.nasa.gov/ye_reports.html. 

Mueller J.M. and G. S. Fargion, 2003: Ocean Optics Protocols for Satellite Ocean Color Sensor Validation, 

Revision 4, Part V, NASA Tech. Memo. 2003-210004/Rev 4/Vol.V, NASA Goddard Space Flight 

Center, Greenbelt, Maryland, 1-25 pp. http://simbios.gsfc.nasa.gov/ye_reports.html. 

Mueller J.M. and G. S. Fargion, 2003: Ocean Optics Protocols for Satellite Ocean Color Sensor Validation, 

Revision 4, Part VI, NASA Tech. Memo. 2003-210004/Rev 4./Vol. VI, NASA Goddard Space Flight, 

Grenbelt, Maryland, 1-148 pp., http://simbios.gsfc.nasa.gov/ye_reports.html. 

Neumann, A., T. Walzel et al., 1995: MOS-IRS Data Processing, Software and Data Products (Preliminary

Documentation), DLR Institute for Space Sensor Technology, DLR Institute for Space Sensor 

Technology. 

Riley T. and S. Bailey, 1998: The Sixth Seawifs/SIMBIOS Intercalibration Round-Robin Experiment

(SIRREX-6), NASA Tech. Memo. 1998-206878, NASA GSFC, Greenblet, Maryland, 26 pp. 

Russell, P.B., J.M. Livingston et al., 1993: Pinatubo and Pre-Pinatubo Optical-Depth Spectra: Mauna Loa 

Measurements, Comparisons, Inferred Particle Side Distributions, Radiative Effects, and Relationships 

to Lidar Data, J. Geophys. Res., 98, D12, 22, 969-22,985. 

Van Heukelem L. C.S. Thomas and P.M. Glibert, 2002: Sources of Variability in Chlorophyll Analysis by 

Fluorometry and High-Performance Liquid Chromatography in a SIMBIOS Inter-Calibration Exercise, 

NASA Tech. Memo. 2002-211606, NASAGSFC, Greenbelt, Maryland, 51 pp., 

http://simbios.gsfc.nasa.gov/ye_reports.html.

Wang, M., 1999: A Sensitivity Study of the SeaWiFS Atmospheric Correction Algorithm: Effects of 

Spectral Band Variations”, Remote Sens.Environ., 67, 348-359. 

Wang, M. and B.A. Franz, “Comparing The Ocean Color Measurements Between MOS and SeaWiFS: A 

vicarious intercalibration approach for MOS”, IEEE Trans. Geosci. Remote Sensing, 38, 184-197, 

2000. 

Wang, M., et al., 2002: Ocean Color Optical Property Data Derived from OCTS and POLDER: A 

Comparison Study, Appl. Optics, Vol. 41, No 6, 974-990. 

Werdell, P.J. and S.W. Bailey, 2002: The SeaWiFS Bio-optical Archive and Storage System (SeaBASS):

Current architecture and implementation, NASA Tech. Memo. 2002-211617, G.S. Fargion and C.R.

McClain, Eds., NASA GSFC, Greenbelt, Maryland, 45 pp. 

Werdell, P.J, S.W. Bailey, G.S. Fargion et al., 2003: Unique Data Repository Facilitates Ocean Color 

Satellite, EOS, Vol. 84, N. 38, 387 and 377. 

26 
SIMBIOS Project Annual Report 

This Research was Supported 

 by the SIMBIOS Project Office 

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. A., D. K. Clark, W. E. Esaias, G. S. Fargion, G. C. Feldman, and C. R. McClain, 2003:

Development of a consistent multi-sensor global ocean color time series, Int. J. Remote Sens., Issue 20 

of Vol. 24, 2047-4067. 

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. 

Fargion G. S. and C. R. McClain, 2000: Three Year of Ocean Color Instrument Intercomparisons and 

Cross-Calibrations by the SIMBIOS Project, Remote Sensing of the Ocean and sea Ice 2000, SPIE vol. 

4172, 44-55. 

Fargion G.S. et al., 2003: SIMBIOS PROGRAM in Support of OCEAN COLOR Mission: 1997-2003, 

SPIE Conference, 3-8 August, San Diego, SPIE # 5155-7 (in press). 

Gregg, W. W., M. E. Conkright, J. E. O'Reilly, F. S. Patt, M. Wang, J. Yoder, and N. Casey-McCabe, 

2002: NOAA-NASA Coastal Zone Color Scanner reanalysis effort, Appl. Opt., 41, 1615-1628. 

Gross-Colzy L., R. J. Frouin, C. M. Pietras, G. S. Fargion, 2002: Nonsupervised classification of aerosol

models for ocean color remote sensing Ocean Remote Sensing and Applications, Editors: R. J. Frouin, 

Y. Yuan, H. Kawamura, Proceedings of SPIE Vol. 4892, [4892-14]. 

Holben B.N., D. Tanre, A. Smirnov, T.F. Eck, I. Slutsker, N. Abuhassan, W.W. Newcomb, J. Schafer, B.

Chatenet, F. Lavenue, Y.F. Kaufman, J. Van de Castle, A. Setzer, B. Markham, D. Clark, R. Frouin, 

R.Halthore, N.A. Karnelli, N.T. O’Neill, C. Pietras, R. Pinker, K. Voss, G. Zibordi, 2001, An emerging 

ground-based aerosol climatology: Aerosol Optical Depth from AERONET, J. Geophys. Res., 106, 12 

067-12 097. 

Ichoku C., R. Levy, Y. J. Kaufman, L. A. Remer, R. Li, V. J. Martins, B. N. Holben , N.K. Abuhassan, I. 

Slutsker, T.F. Eck, C. Pietras, 2002: Analysis of the performance characteristics of the five-channel

Microtops II Sunphotometer for measuring aerosol optical thickness and precipitable water vapor, J. 

Geophys. Res, Vol. 107, D13, 10.1029 

Johnson B. C., S. Brown, K.Lykke, C. Gibson, G. S. Fargion et al., 2003: Comparison of Crygenic

Radiometry and Thermal Radiometry Calibrations at NIST Using Multichannel Filter Radiometers, 

Metrologia, 40, S70-S77. 

Knoblelspiesse K., C. Pietras and G.S. Fargion, 2003: Error Correction for Sea Deployment of Microtops 

II Hand Held Sun Photometer, Journal of Atmospheric & Oceanic Tech., Vol. 20, 767-771 

Knobelspiesse, K.D., C. Pietras, G.S. Fargion, M. Wang, R. Frouin, M.A. Miller, A. Subramaniam and 

W.M. Balch, 2003b: Maritime aerosol optical properties measured by handheld sun photometers. 

Submitted to Remote Sensing of Environment 

27 
SIMBIOS Project Annual Report 

Kwiatkowska E. J. and G. S. Fargion, 2002: Merger of Ocean Color Information from Multiple Satellite 

Missions under the NASA SIMBIOS Project Office. Proceedings of the Fifth International Conference 

on Information Fusion, Annapolis, MD, USA, 1, 291-298. 

Kwiatkowska E. J. and G. S. Fargion, 2002: Merger of Ocean Color Data from Multiple Satellite Missions 

within the SIMBIOS Project. Proceedings of SPIE Symposium – Remote Sensing of the Atmosphere, 

Ocean, Environment, and Space, Oct., Hangzhou, China, Ocean Remote Sensing and Applications, 

4892, 168-182. 

Kwiatkowska, 2003: Application of Machine Learning Techniques Toward the Creation of Consistent and 

Calibrated Global Chlorophyll Concentration Baseline Dataset Using Remotely Sensed Ocean Color 

Data, IEEE Trans. Geocience and Remote Sensing, vol. 41, no. 12, (in press) 

Legrand M., C. Pietras, G. Brogniez, M. Haeffelin, N.K. Abuhassan, and M. Sicard, 2000, A HighAccuracy Multiwavelength Radiometer for In Situ Measurements in the Thermal Infrared. Part I: 

Characterization of the instrument, J. Atmos. Ocean. Technol., Vol. 17, 1203-1214. 

Meister G., P. Abel, R. Barnes, J. Cooper, C.Davis, M.Godin, G. S. Fargion et al, 2003: Comparison of 

Spectral Radiance Calibrations at Oceanographic and Atmospheric Research Laboratories, Metrologia, 

40, S93-S96 

Mueller, J., C. McClain, R. Caffrey, and G. Feldman, 1998: The NASA SIMBIOS Program, Backscatter.

Porter J.N., M. Miller, C. Pietras, C. Motell, 2001, Ship-Based Sun Photometer Measurements Using 

Microtops Sun Photometers, J. Atmos. Ocean. Technol., Vol. 18, 765-774. 

Smirnov, A., B.N.Holben, O.Dubovik, D.L.Westphal, A.K.Goroch, C.R.McClain, T.F.Eck, and I.Slutsker, 

C. Pietras, 2002: Atmospheric aerosol optical properties in the Persian Gulf region, J. Atmos. Sci., 59, 

620-634. 

Smirnov, A., B.N.Holben, Y.J. Kaufman, O.Dubovik, T.F.Eck, and I.Slutsker, C. Pietras, R.N. Halthore, 

2002: Optical properties of Atmospheric aerosol in the Maritime Environments, J. Atnos. Sci., 59, 501-

523. 

Tanaka, T. and M. Wang, Solution of radiative transfer in anisotropic plane-parallel atmosphere, J. Quant. 

Spectr. Rad. Trans. (In press). 

Wang, M., 1999: Atmospheric correction of ocean color sensors: Computing atmospheric diffuse 

transmittance, Appl. Opt. , 38, 451-455. 

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, Vol. 81, 197. 

Wang, M., A. Isaacman, B. Franz, and C. R. McClain, 2002: Ocean Color Optical Property Data Derived 

from OCTS and POLDER: A Comparison Study, Appl. Optics, Vol. 41, No 6, 974-990. 

Wang, M. and H. R. Gordon, 2002: Calibration of ocean color scanners: How much error is acceptable in 

the near-infrared, Remote Sens. Environ., 82, 497-504. 

Werdell J., S. Bailey, G.S.Fargion et al., 2003: Unique Data Repository Facilates Ocean Color Satellite 

Validation, EOS, Vol 84, n.38, 377 & 387. 

28 
SIMBIOS Project Annual Report 

Zhao T. X-P., I. Laslo, B. Holben, C. Pietras, and K. Voss, 2003: Validation of two-channel VIRS

retrievals of aerosol optical thickness over ocean and quantitative evaluation of the impact from

potential subpixel cloud contamination and surface, J. Geophys. Res, 108(D3), 4106. 

Zhao, T. X.-P., I. Laszlo, O. Dubovik, B. N. Holben, J. Sapper, D. Tanré, and C. Pietras, 2003: A study of 

the effect of non-spherical dust particles on the AVHRR aerosol optical thickness retrievals, Geophys. 

Res. Lett., 30(6), 1317. 

NASA Techical Memorandum

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. 

McClain, C. R., W. Esaias, G. Feldman, R. Frouin, W. Gregg, and S. Hooker, 2002: The Proposal for the 

NASA Sensor Intercalibration and Merger for Biological and Interdisciplinary Oceanic Studies 

(SIMBIOS) Program, 1995, NASA/TM-2002-210008, NASA Goddard Space Flight Center, Greenbelt, 

Maryland, 63 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. 

Fargion G.S. and R.C. McClain, 2001: SIMBIOS Project 2000 Annual Report, NASA Tech. Memo. 2001-

209976, NASA Goddard Space Flight Center, Greenbelt, Maryland, 1-164 pp. 

Fargion G.S., R. Barnes and R.C. McClain, 2001: In situ AOT collected by the SIMBIOS Program (1997-

2000): protocols, and data QC and analysis, NASA Tech. Memo. 2001-209982, NASA Goddard Space 

Flight Center, Greenbelt, Maryland, 1-103 pp. 

Fargion G.S. and R.C. McClain, 2002: SIMBIOS Project 2001 Annual Report, NASA Tech. Memo. 2002-

210005, NASA Goddard Space Flight Center, Greenbelt, Maryland, 1-184 pp. 

Fargion G.S. and R.C. McClain: 2003: SIMBIOS Project 2002 Annual Report, NASA Tech. Memo. 2003-

210005, NASA Goddard Space Flight Center, Greenbelt, Maryland, 1-184 pp. 

Meister, G., and others, 2002: The First SIMBIOS Radiometric Intercomparison (SIMRIC-1), AprilSeptember 2001, NASA/TM2002-210006, NASA Goddard Space Flight Center, Greenbelt, Maryland, 

60 pp. 

Meister G. et al., 2003: The Second SIMBIOS Radiometric Intercomparison (SIMRIC-2), MarchNovember 2002, NASA Tech. Memo. 2003-01930, GSFC, Greenbelt, Maryland, 1-71 pp. 

Mueller J.M. and G. S. Fargion, 2002: Ocean Optics Protocols for Satellite Ocean Color Sensor Validation, 

Revision 3, Part I, NASA Tech. Memo. 2002-210004, NASA Goddard Space Flight Center, Greenbelt, 

Maryland, 1-137 pp. 

Mueller J.M. and G. S. Fargion, 2002: Ocean Optics Protocols for Satellite Ocean Color Sensor Validation, 

Revision 3, Part II, NASA Tech. Memo. 2002-210004, NASA Goddard Space Flight Center, Greenbelt, 

Maryland, 138-308 pp. 

29 
SIMBIOS Project Annual Report 

Mueller J.M. and G. S. Fargion, 2003: Ocean Optics Protocols for Satellite Ocean Color Sensor Validation, 

Revision 4, Part I, NASA Tech. Memo. 2003-210004/Rev 4/Vol.I, NASA Goddard Space Flight Center, 

Greenbelt, Maryland, 1-50 pp. 

Mueller J.M. and G. S. Fargion, 2003: Ocean Optics Protocols for Satellite Ocean Color Sensor Validation, 

Revision 4, Part II, NASA Tech. Memo. 2003-210004/Rev 4/Vol. II, NASA Goddard Space Flight 

Center, Greenbelt, Maryland, 1-56 pp. 

Mueller J.M. and G. S. Fargion, 2003: Ocean Optics Protocols for Satellite Ocean Color Sensor Validation, 

Revision 4, Part III, NASA Tech. Memo. 2003-210004/Rev 4./Vol.III, NASA Goddard Space Flight 

Center, Greenbelt, Maryland, 1-78 pp. 

Mueller J.M. and G. S. Fargion, 2003:Ocean Optics Protocols for Satellite Ocean Color Sensor Validation, 

Revision 4, Part V, NASA Tech. Memo. 2003-210004/Rev 4/Vol.V, NASA Goddard Space Flight 

Center, Greenbelt, Maryland, 1-25 pp. 

Mueller J.M. and G. S. Fargion, 2003: Ocean Optics Protocols for Satellite Ocean Color Sensor Validation, 

Revision 4, Part VI, NASA Tech. Memo. 2003-210004/Rev 4./Vol. VI, NASA Goddard Space Flight, 

Greenbelt, Maryland, 1-148 pp. 

Van Heukelem, L., C. S. Thomas, and P. M. Glibert, 2002: Sources of Variability in Chlorophyll Analyseis 

by Fluorometry and High-Performance Liquid Chromatography in a SIMBIOS Inter-Calibration 

Exercise, G. S. Fargion and C. R. McClain (eds.), NASA Tech. Memo. 2002-211606, Goddard Space 

Flight Center, Greenbelt, Maryland, 50 pp. 

Werdell, P. J., and S. W. Bailey, 2002: The SeaWiFS Bio-Optical Archive and Storage System (SeaBASS):

Current Architecture and Implementation, G. S. Fargion and C. R. McClain (eds.), NASA Tech. Memo 

2002-211617, Goddard Space Flight Center, Greenbelt, Maryland, 45 pp. 

Presentations 

Bailey, S. W., J. Werdell and C. R. McClain, 2001: Validation of Satellite-Derived Ocean Color: Theory 

and Practice, EOS. Trans. AGU, 82(47), Fall Meet., Suppl., Abstract OS52A-0520, 2001. 

Bailey S. W., P. J. Werdell, C. McClain and G. Fargion, 2000: Validation of SeaWiFS-derived 

Chlorophyll: Comparisons at Several Spatial and Temporal Scales, Venice 2000, Italy 

Bailey S., C. Pietras, K. Knobelspiesse, G. Fargion, and C. McClain, SeaWiFS Aerosol Product Compared 

to Coastal and Island In situ Measurements, Eos Trans. AGU, 83(19), Spring Meet. Suppl., Abstract

A51B-08, 2002. 

Barnes R., D. Clark, W. Esaias, G. S. Fargion, G. Feldman, C. R. McClain, 2001: Requirements for a selfconsistent multi-sensor global ocean color time series” Proceedings of the International Workshop on 

Geo-Spatial Knowledge Processing for Natural Resource Management, Varese, Italy, 28-29 June 

2001, 13-18. 

Bartholomew M. J., M. Miller, G.S. Fargion, S. Bayley and R. Reynolds, 2001: Comparison of Satellite 

Estimates of Aerosol Optical Thic ness and Cloud Cover with Shipboard Measurements, Eos Trans. 

AGU, 82(47), Fall Meet. Suppl., Abstract OS51E-11. 

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. 

30 
SIMBIOS Project Annual Report 

Fargion G. S., C. R. McClain and Robert A. Barnes, 2000: Ocean Color Instrument Intercomparisons and 

Cross-Calibrations 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 

Cross-Calibrations by the SIMBIOS Project (1997-2000), Conference on Remote Sensing of the Ocean 

and Sea Ice VI, Barcelona. 

Fargion G.S. et al., 2003: SIMBIOS PROGRAM in Support of Ocean Color Mission: 1997-2003”, SPIE 

Conference, 3-8 August, San Diego. 

Franz, B. A., 1998: A Simple Destriping Algorithm for MOS Images. Proc. of the 2nd International

Workshop on MOS-IRS and Ocean Color, Berlin. 

Franz, B. A., 1999: Status of the MOS Ground Station at NASA/Wallops. Proc. of the 3rd International

Workshop on MOS-IRS and Ocean Color, Berlin. 

Franz, B. A., 1999: An Overview of SeaWiFS Mission. International Symposoium on Ocean color Remote 

Sensing and Carbon Flux by CERES and JUWOC, Chiba, Japan. 

Franz B. A. and J. M. Gales, 2001: A Long Term Intercomparison of MOS and SeaWiFS, 4th Berlin

Workshop on Remote Sensing “5Years of MOS-IRS”, 9-16. 

Gales J., B. Franz and M. Wang, 2001: A Three Year Intercomparison of Oceanic Optical Properties from

MOS and SeaWiFS, EOS. Trans. AGU, 82(47), Fall Meet., Suppl., Abstract OS52A-0513, 2001 

Gross, L, R. Frouin, C. Pietras, K.D. Knobelspiesse and G.S. Fargion, 2002: Non-supervised Classification 

of Ground-based Radiometer Retrievals in Order to Assess the Natural Distribution of Aerosol Volume

Size Distributions and Refractive Indexes, Spring 2002 American Geophysical Union Meeting, 

Washington, DC. 

Gross L., R. Frouin, C. Pietras and G.S. Fargion, 2002: Non-Supervised Classification of aerosol Mixtures 

for ocean Color Remote Sensing, Proceedings of the "Remote Sensing of the Atmosphere, Ocean, 

Environment and Space Proc. of SPIE Symposium, Hangzhou, China. 

Kilpatrick, K., E. Kearns, E.J. Kwiatkowska-Ainsworth, and R.L. Evans, 2002: Time Series of Calibrated 

Ocean Products from NASA's Moderate Resolution Scanning Spectrometer (MODIS), Proceedings of 

the Ocean Sciences Meeting, Honolulu, Hawaii. 

Kim Y. , S. Yoo, Byung-Ju Sohn , G. S.Fargion et al., 2001: The OSMI Post-launch Calibration”, Eos 

Trans. AGU, 82(47), Fall Meet. Suppl., Abstract OS42D-08. 

Knobelspiesse K., C. Pietras and G.S. Fargion, 2001: MicroTops II Hand-held Sun Photometers Sun 

Pointing Error Correction for Sea Deployment, EOS. Trans. AGU, 82(47), Fall Meet., Suppl., Abstract

OS52A-0532, 2001. 

Knobelspiesse, K.D., C. Pietras, M. Miller, M. Reynolds, R. Frouin, P. Quinn, P-Y. Deschamps, J. Werdell, 

and G.S. Fargion, 2002: Comparison of In Situ Aerosol Data from the ACE-Asia 2002 Experiment, 

Spring 2002 American Geophysical Union Meeting, Washington, DC. 

Kwiatwska-Ainsworth, 2001; Merger of Ocean Color Information of Different Spatial Resolution: 

SeaWiFs and MOS,EOS. Trans. AGU, 82(47), Fall Meet., Suppl., Abstract OS52A-0514, 2001. 

31 
SIMBIOS Project Annual Report 

Kwiatkowska E. and G.S. Fargion, 2002: Merger of Ocean Color Data from Multiple Satellite Missions 

within the SIMBIOS Project, Proceedings of SPIE's Symposium on Remote Sensing of the 

Atmosphere, Ocean, Environment, and Space, Hangzhou, China. 

Kwiatkowska E. and G.S. Fargion, 2002: Merger of Ocean Color Information from Multiple Satellite 

Missions under the NASA SIMBIOS Project Office, Proceedings of the Fifth International Conference 

on Information Fusion, Annapolis, Maryland, 291-298. 

Kwiatkowska-Ainsworth, E. 2001: Merger of Ocean Color Information of Different Spatial Resolution: 

SeaWiFS and MOS, Eos Trans. American Geophysical Union, vol. 82, no. 47, F675 (OS52A-0514). 

Isaacman, A., B. A. Franz, and R. E. Eplee, Jr., 1999: An Investigation of Time Variability in-waterLeaving Radiances Retrieved from the Ocean Color Measurements, ALPS 99 Conference, Méribel, 

France. 

McClain R. C., G.S. Fargion and G. Feldman, 2001: Overview of Ocean Color Calibration and Validation 

Efforts, Eos Trans. AGU, 82(47), Fall Meet. Suppl., Abstract OS42D-01. 

Meister, G., G.S. Fargion and C.R. McClain, 2002: Monitoring the Radiometric Stability of the SeaWiFS 

Transfer Radiometer II, Spring 2002 American Geophysical Union Meeting, Washington, DC. 

Meister G., P. Abel , J. Cooper, C. Davis, R. Frouin, D. Goebel, M. Godin, D. Korwan, S. McLean, R.

Maffione, D. Menzies, A. Poteau, J. Robertson, G. S. Fargion and C. R. McClain, 2001: Results from

a Round-Robin-Comparison of Radiance Calibrations at Oceanographic and Atmospheric Research 

Laboratories, Eos Trans. AGU, 82(47), Fall Meet. Suppl., Abstract OS52A-0534 . 

Morrison R., H. Sosik, S. Gallager and G.S.Fargion, 2001: Time Series Measurements and Algorithm Dev 

lopment at the FRONT Site on the New England Continental Shelf, Eos Trans. AGU, 82(47), Fall 

Meet. Suppl., Abstract OS52A-0525 

Pietras C.M., Ainsworth E., Bailey S., Hsu C., Wang M., Fargion G., McClain C., Abuhassan N., and 

Holben B., 2000: Aerosol Properties from Field Measurements Using Hand-Held and Automatic Sun 

Photometers, AGU Spring Meeting Washington D.C. May 30-June 3. 

Pietras C., E. Ainsworth, S.W. Bailey et 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. 

Pietras C., Frouin R., Nakajima T., Yamano M., Knobelspiesse K., Werdell J., Meister G., Fargion G., and 

McClain C., 2001: Aerosol Properties Derived from the PREDE POM-01 Mark II Sun Photometer, 

EOS. Trans. AGU, 82(47), Fall Meet., Suppl., Abstract OS52A-0531, 2001. 

Pietras, C, K.D. Knobelspiesse, S. Bailey, J. Werdell, R. Frouin, M. Miller, G.S. Fargion and C.R. 

McClain, 2001: SeaWiFS Aerosol Product Comparied to In Situ Measurements Over Open Oceans, 

Spring 2002 American Geophysical Union Meeting, Washington, DC. 

Smirnov A., B. Holben, R. Frouin, G.S. Fargion et al., 2001: Maritime Aerosol Optical Model Based on the 

Aerosol Robotic Network (AERONET)Measurements, Eos Trans. AGU, 82(47), Fall Meet. Suppl., 

Abstract OS51E-12. 

Souaidia N., C. Pietras, S. Brown, K. Lykke, G. Fargion, B. C. Johnson, R. Frouin, P.Y. Deschamps, 2002:

Radiometer laser and lamp based radiometric calibrations; comparison with the Langley technique and 

implications on remote sensing, EOS Trans. AGU, 83(47), Fall Meet. Suppl., Abstract A52A-0098, 

2002

32 
SIMBIOS Project Annual Report 

Vermeulen A., Devaux C., Tanre D., Herman M., Holben B., Blarel L., Chatenet B., and Pietras C., 2001:

AERONET Polarization Measurements, AGU Fall Meeting San Francisco. December 10-14. 

Wang, M. and B. A. Franz, 1998: A Vicarious Intercalibration between MOS and SeaWiFS, Proc. 2nd 

International Workshop on MOS-IRS and Ocean Colour, Berlin, Germany, 95-102. 

Wang, M., 1998: A validation of the SeaWiFS O2 A-band absorption correction, Proc. the 4th Pacific 

Ocean Remote Sensing Conference, Qingdao, China, 32-36, 1998. 

Wang, M., 1998: Applying the SeaWiFS atmospheric correction algorithm to MOS, Proc. the 4th Pacific 

Ocean Remote Sensing Conference, Qingdao, China, 88-91, 

Wang, M., S. Bailey, C. R. McClain, C. Pietras and J. T. Riley, 1999: Remote Sensing of the Aerosol

Optical Thickness from SeaWiFS in Comparison with In situ Measurements. ALPS 99 Conference, 

Méribel, France. 

Wang, M., A. Isaacman, B. A. Franz, and C. R. McClain, 2001: A comparison study of the ocean color data 

derived from OCTS and POLDER," The AGU Fall Meeting, San Francisco, California, December 10-

14. 

Werdell P. J., S. W. Bailey and G.S. Fargion, 2001: The Architecture and Utility of SeaBASS: the 

SeaWiFS Bio-Optical Archive and storage System, EOS. Trans. AGU, 82(47), Fall Meet., Suppl., 

Abstract OS52A-0533, 2001. 

33 
SIMBIOS Project Annual Report 

Chapter 2 

SIMBIOS: Science Team and Contracts 

Giulietta S. Fargion 

 Science Applications International Corporation (SAIC), Beltsville, Maryland 

2.1 SCIENCE TEAM 

The SIMBIOS Science Team was selected through two NASA Research Announcements (NRA), in

1996 and 1999. NASA HQ managed the process of team selection, while the Goddard Space Flight Center 

(GSFC) NASA Procurement Office handled the team contracts, work statements and, if necessary, budget

negotiations. The Project funded numerous US investigators and collaborated with several international

investigators, space agencies (e.g., NASDA, CNES, KARI, etc.) and international organizations (e.g., 

IOCCG, JRC). US investigators were under contract to provide in situ atmospheric and bio-optical data

sets, as well as 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 go into a 

database and are shared with the PI’s institution or upper management. Chapters 3 to 19 contain the 

individual PI’s contributions and describe the funded research topics, field study activities, and results of 

concluded research. These chapters are reproduced as submitted with minimal editing by the Project Office. 

2.2 CONTRACT OVERVIEW 

The third-year of the SIMBIOS NRA-99 contracts ended on November 30, 2003. The Project Office 

followed the same procedure used in 1999 before compiling the final evaluations. The four categories to be 

evaluated are suggested in the “Evaluation of Performance” from the Federal Acquisition Regulation 

(FAR) 42.15 and NASA FAR Supplement (NSF) 1842.15 or NASA form 1680 used by GSFC. Under the 

“quality” category the following are considered: 

• data quality and completeness; 

• ancillary information provided on the data (metadata); 

• the data's usefulness in relation to SIMBIOS goals, i.e., calibration, validation, and algorithm

development; and 

• quality of technical reports. 

The “time” category is a mixed bag, but is viewed with respect to data and documentation (monthly

and year-end reports, and special topic publications) and delivery times. Under the “other” category is 

considered: 

• scientific publications and scientific achievements; 

• science team collaboration and involvement; and 

• other significant events occurring during the contract period evaluated. 

As a result of the formal final evaluation with the SIMBIOS PIs, all investigators were evaluated as 

very good or excellent. The GSFC Procurement Office implemented and executed one no cost exstension 

option. Further details on the research status and progress reports can be found on our web site at

http://simbios.gsfc.nasa.gov/status.html.

34 
SIMBIOS Project Annual Report 

Chapter 3 

Adaptation of a Hyperspectral Atmospheric Correction 

Algorithm for Multi-spectral Ocean Color Data in 

Coastal Waters 

Bo-Cai Gao, Marcos J. Montes, and Curtiss O. Davis 

Remote Sensing Division, Naval Research Laboratory, Washington, D.C. 

3.1 INTRODUCTION 

This SIMBIOS contract supports several activities over its three-year time-span. These include certain 

computational aspects of atmospheric correction, including the modification of our hyperspectral

atmospheric correction algorithm Tafkaa for various multi-spectral instruments, such as SeaWiFS, MODIS, 

and GLI. Additionally, since absorbing aerosols are becoming common in many coastal areas, we are 

making the model calculations to incorporate various absorbing aerosol models into tables used by our 

Tafkaa atmospheric correction algorithm. Finally, we have developed the algorithms to use MODIS data to

characterize thin cirrus effects on aerosol retrieval. 

3.2 RESEARCH ACTIVITIES 

One of our accomplishments this year included further modifications of our hyperspectral atmospheric 

correction algorithm (Gao et al. 2000; Montes et al. 2001) suggested at the 2002 year-end review, as well as 

others that allow us flexibility in examining the results of different aerosol corrections applied to different

scenes. 

One of the inputs for Tafkaa is the wind speed. This is used to aid in correcting for the specular 

reflections from the ocean surface. Previously we had the ability to only read one wind speed value for the 

whole scene; this does not make much sense for typical MODIS and SeaWiFS scenes that span a 

reasonably large surface area. Tafkaa can now read an input data plane that has the wind speed, and it can 

use that information for each pixel. This does not tend to slow Tafkaa too much, although it does require 

more memory since the complete lookup tables for each wind speed need to be held in memory. Similar 

modifications were made for ozone amount, so Tafkaa may be run either for a single ozone value for the 

whole scene, or with an ozone input array provided. 

Other modifications to Tafkaa include the ability to exclude certain classes of aerosol from

consideration, the ability to force Tafkaa to use a certain aerosol model and optical depth, and the ability to

solve only for aerosol optical depth when given a fixed aerosol model. Tafkaa now has the ability to ignore 

masked data, which makes it faster in areas where the clouds and land have been masked. Finally, an 

absorbing urban aerosol has been added to the lookup tables, which is useful in many areas, including off 

the east coast of both North America and Asia. 

These modifications are on top of the previously reported modifications from the first two years of our 

report: making a fast, portable (the source code has been compiled on multiple architecture/OS/compiler 

platforms), modular version of Takfaa using the geometry on a pixel-to-pixel basis. 

We participated in the field observations of Asian Dust events over the Monterey Bay in April 2003, 

along with many other researchers in the ADAM (Asian Dust Above Monterey) collaboration. We

obtained some imagery with the PHILLS; unfortunately, it was not on the best days (in terms of the 

presence of Asian dust), and so we were not able to fly above, in, and below the dust layers, as was 

originally planned. Our collaborators did sample the Asian dust, where the layers were located, etc., and we 

anticipate being able to use the results of their research in the future to characterize the Asian dust that 

crosses the Pacific. This will allow us to build some aerosol lookup tables for similar events. 

35 
SIMBIOS Project Annual Report 

We are still in the process of calculating the Asian dust aerosol models, a project which has run behind

schedule. We are in the process of writing papers about our research results. Of particular interest are the 

scenes at the time of the 2001 Leo-15 experiment. 

3.3 RESEARCH RESULTS 

During the first year of the SIMBIOS program we analyzed scenes from April, 2000, when AVIRIS 

flew under MODIS and SeaWiFS over the MOBY site (see Figures 3.1 & 3.2). We developed software to

navigate the AVIRIS scene, and additional processing software to transfer the AVIRIS radiance 

measurements to space. We found that near 0.47 micron, the observed reflectance (corrected to the top of 

the atmosphere) for AVIRIS is 9% greater than that measured by MODIS; at 0.747 micron the difference is

about 8%, with the MODIS values being greater than those of AVIRIS. Comparisons of water-leaving 

reflectance showed that MOBY agrees quite well with MODIS, but not with AVIRIS. This was, however, 

based on a single underflight, and the results needs to be revisited as all the instruments have different

calibrations now than in the past. 

The modifications to the base multi-spectral version of Tafkaa have continued. The modifications

allow Tafkaa to execute more quickly, as well as increasing the types of aerosols available. As these 

modifications have occurred in the base version, it takes little more than recompiling the source code in

order to propagate the modifications to the executables for the sensor-specific versions of Tafkaa. 

We have processed several scenes from MODIS and SeaWiFS data with the pixel-to-pixel versions of

the atmospheric correction code. For MODIS scenes, our algorithm and Gordon’s algorithm return very

similar results over areas of the open ocean. Our algorithm makes use of both the ocean channels of 

MODIS, as well as the land channels. Use of the land channels allows us to perform atmospheric 

corrections in certain turbid areas where the normal algorithms fail – for example, in areas with enough 

sediment so that there is measurable signal at 0.75 micron (see Figure 3.3). MODIS land channels also give 

us flexibility in presenting spectra when the ocean-color channels saturate, as can happen in certain 

circumstances as in some turbid waters as well as in some regions with coccolithophores (see Figures 3.4 & 

3.5). For SeaWiFS scenes, our results tend to differ. First, the SeaWiFS correction uses the 0.765 mm band, 

a band that encompasses the O2 A-band absorption feature. Because of the presence of the strong 

absorption feature, it is difficult (impossible?) for our algorithm to use this band as the basis of atmospheric 

correction. Likewise, maps of the 0.665 mm band show that coastal ocean color features are still visible in 

that band, making it impossible to use that band for atmospheric correction. 

Other areas of research include B.-C. Gao’s collaborations to study methods of discriminating the 

effects of cirrus clouds and aerosols at longer wavelengths (Gao et al., 2002) and algorithms for masking 

sediment laden waters where it is necessary to use longer wavelengths to achieve any atmospheric 

correction (Li et al., 2003). 

Products

We have shared previous versions of our algorithms with groups at GSFC and NRL-Stennis. Current

source code, executables, look-up tables, and user guides for the atmospheric correction algorithms will be 

provided to the SIMBIOS office. 

3.4 CONCLUSIONS 

We have made progress in modifying our atmospheric correction algorithm to be used with multispectral data from MODIS and SeaWiFS. Our algorithm takes advantage of MODIS’ long wavelength and 

land bands to provide atmospheric correction over brighter ocean scenes including sediments and 

coccolithophore blooms. These algorithms now use the complete geometry of each pixel, incorporate new 

aerosols, and allow for ozone and wind speed to vary on a pixel-by-pixel basis. Our results are promising, 

especially over turbid areas. We are in the process of refining our algorithms and are involved in testing 

with both groups working with the SIMBIOS program at GSFC and NRL-Stennis. 

36 
SIMBIOS Project Annual Report 

Figure 3.1: Top of the atmosphere reflectance observed near the MOBY site by AVIRIS (dashed line) and 

MODIS (open triangles). Note that while the radiance measurements are generally in good agreement, the 

AVIRIS radiance near 0.4 micron is significantly less than radiance observed by MODIS. The AVIRIS radiance 

at 0.747 micron is also about 10% less than that measured MODIS. The solid line with solid diamonds is the 

observed atmospheric reflectance before being transferred to the top of the atmosphere. 

Figure 3.2: Derived water-leaving reflectance spectra from AVIRIS and MODIS (open triangles), compared to

MOBY (open circles, small dashes) measurements. The solid line is AVIRIS corrected using the two MODIS 

bands near 0.75 micron and 0.865 micron to determine the atmospheric correction. The long-dashed line is the 

atmospheric correction derived using four window channels longer than 1 micron. 

37 
SIMBIOS Project Annual Report 

Figure 3.3: Atmospherically corrected reflectance from a MODIS image off the coast of Eastern Australia. The 

long dashes connect the sets of land channels (open diamonds), and the short dashes connect sets of ocean 

channels (plusses). These results were derived using some of the land channels and the pixel-to-pixel version of 

Tafkaa to determine an atmospheric correction. Note, in particular, that our results yield significant water leaving

radiance at 0.75 micron. Water leaving reflectance cannot be retrieved for pixels like this one using Gordon’s 

algorithm. 

Figure 3.4: The at-sensor apparent reflectance from a MODIS image of the English Channel, 2000 June 25, 

comparing results from land and ocean channels from two different pixels in a coccolithophore bloom. The long 

dashes connect the land channels, and the short dashes connect ocean channels. The open diamonds (land 

channels) and plusses (ocean channels) are from pixel A, which is clearly saturated in the fifth and seventh ocean 

channels from the left. Spectra from nearby unsaturated pixel B are shown with open triangles (land channels) and 

asterisks (ocean channels). 

38 
SIMBIOS Project Annual Report 

Figure 3.5: Atmospherically corrected reflectance from the same locations as in Figure 4. The symbols are 

the same as in Figure 4. Note the quite reasonable agreement between the atmospherically corrected land 

channels and ocean channels except for the two saturated channels. 

REFERENCES 

Gao, B.-C., M.J. Montes, Z. Ahmad, and C.O. Davis, 2000. Atmospheric correction algorithm for 

hyperspectral remote sensing of ocean color from space. Appl Opt., 39(6), 887-896. 

Montes, M.J., B.-C. Gao, and C.O. Davis. 2001: A new algorithm for atmospheric correction of 

hyperspectral remote sensing data. SPIE, 4383, 23-30. 

This Research was Supported by 

the NASA Interagency Agreement S-44797-X 

Publications 

Gao, B.-C., Y. J. Kaufman, D. Tanre, and R.-R. Li, 2002: Distinguishing tropospheric aerosols from thin

cirrus clouds for improved aerosol retrievals using the ratio of the 1.38 micron and 1.24 micron 

channels, Geophysical Research Letters, 29, 1890, 2002. 

Li, R.-R., Y. J. Kaufman, B.-C. Gao, and C. O. Davis, Remote sensing of suspended sediments and shallow 

coastal waters, IEEE Trans. Geosci. Remote Sensing, 41, 559-566, 2003. 

Presentations 

Montes, M. J., B.-C. Gao, and C. O. Davis 2003: Tafkaa atmospheric correction of hyperspectral and 

multispectral data, a talk presented 2003 Aug. 6 by M. J. Montes at SPIE’s 48th Annual Meeting in 

San Diego, CA. 

Montes, M. J., C. O. Davis, and B.-C. Gao 2003, Hyperspectral Imaging and Atmospheric Correction, a 

lecture presented 2003 July 9 by M. J. Montes to the “Spatio-Temporal Statistical Analysis of Multi39 
SIMBIOS Project Annual Report 

platform Optical Ocean Observations” class at the University of Maine’s Darling Marine Center, 

Walpole, Maine. 

Montes, M. J., C. O. Davis, B.-C. Gao, and M. Moline 2003: Analysis of AVIRIS Data from Leo-15 and 

Other sites using Tafkaa atmospheric correction , a talk presented 2003 Feb. 27 by M. J. Montes at the 

2003 AVIRIS Earth Science and Applications Workshop in Pasadena, CA. 

Gao, B.-C., M. J. Montes, and C. O. Davis, 2003: Adaptation of a Hyperspectral Atmospheric Correction 

Algorithm for Multi-Spectral Ocean Color Data in Coastal Waters, a poster at the Feb. 2003 MODIS 

workshop in New Hampshire. 

Davis, C.O., B.-C. Gao, M.J. Montes, G. Feldman, B. Franz, F. Patt, and R. Stumpf, 2001: Atmospheric 

Correction of Ocean Color Data at the MOBY site in Hawaiian Waters. Presented at the Tenth JPL 

AVIRIS Science and Applications Workshop, Pasadena. 

40 
SIMBIOS Project Annual Report 

Chapter 4 

Bio-Optical Measurements in Upwelling Ecosystems in 

Support of SIMBIOS 

Francisco P. Chavez, Peter G. Strutton, Victor S. Kuwahara, Kevin L. Mahoney, and Eric 

Drake 

Monterey Bay Aquarium Research Institute, Moss Landing, California 

4.1 INTRODUCTION

The upwelling region of the equatorial Pacific Ocean, which spans one quarter of the earth’s 

circumference, strongly impacts global biogeochemistry. This upwelling system 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, 2000). In addition, the 

region represents a vast oceanic (case 1) region over which validation data for SeaWiFS are needed. This 

project consists of an optical mooring program and cruise-based measurements focused on measuring 

biological and chemical variability in the equatorial Pacific and obtaining validation data for SeaWiFS.

Since 1996, the MBARI equatorial Pacific program has demonstrated ability to: 

1. obtain high quality, near real time measurements of ocean color from moored platforms in the 

equatorial Pacific. 

2. process and quality control these data into files of the required format for the SeaBASS database. 

3. obtain robust ship-based optical profiles and pigment concentration measurements, also for 

submission to SeaBASS. 

4. process and interpret the time series, satellite and ship-based data in order to quantify the 

biogeochemical processes occurring in the equatorial Pacific on time scales of days to years 

(Chavez et al., 1998; Chavez et al., 1999; Strutton & Chavez, 2000; Strutton et al., 2001, Ryan et 

al., 2002). 

4.2 RESEARCH ACTIVITIES 

Moorings 

The arrangement of the moored MBARI bio-optical and chemical instruments positioned at 0°, 155°W

and 2°S, 170°W has previously been described (Chavez et al. 1998, Chavez et al. 1999, McClain and 

Fargion 1999). The moorings are two of ~70 which form the Tropical Atmosphere Ocean (TAO) array. 

From these locations, daily local 10 am and noon (approximate time of MODIS and SeaWiFS overpasses, 

respectively) bio-optical and chemical data are transmitted via service ARGOS in near real time to

MBARI, and then presented on the internet at: http://bog.shore.mbari.org/~bog/oasis.html. 

Higher frequency, publication-quality data (15-minute intervals) are also recovered at approximately 

six month intervals, and sent to the SeaBASS database after thorough quality control. Derived products, 

such as water leaving radiance (Lw), and remote sensing reflectance (Rrs) are incorporated into these data 

files for validation efforts. In previous years, Lw has been determined through three different methods 

(McLain and Fargion, 1999b), but Lw is now computed only as follows. The diffuse attenuation coefficient

(Kλ) throughout 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 (Lu0m-) and multiplied by 0.544 (a scaling factor

that considers the transmission of light across the air-sea interface) to obtain Lu0m+. Of the three methods 

previously used, this has been shown to be the most reliable, primarily because the 3m+ and 20m41 
SIMBIOS Project Annual Report 

instruments are less susceptible to fouling. With the addition of a 10 m hyperspectral radiometer, the 

efficacy of this method can now be assessed. 

During 2003 our data processing, quality control and data provision capabilities have improved. The 

following quality control procedures are currently employed: 

1. Measured surface-incident irradiance (Es) can not be greater than 1.15 times modeled, clear-sky Es

(Frouin, 1989). 

2. Kλ must be greater than that of pure water (Morel, 1988). 

3. OC4V4 chlorophyll is computed for the Rrs ratios of 412/555, 443/555, 490/555, and 510/555 and the 

coefficient of variance is determined for each wavelength combination. Coefficients of variance greater 

than 0.4 are not acceptable. 

4. Time series of all parameters at all wavelengths for all individual instrument deployments are plotted 

together as one long time series to identify discontinuities between deployments, due to problems such 

as vandalism, fouling and calibration issues. Normalizing specific wavelengths against others is also 

used to identify discontinuities. 

With the buoy design and data collection methods now verified in the field, we are making significant

advancements to the quantity and quality of optical data collected during this funding period. In October 

2002, HOBILabs HR3 hyperspectral radiometers were deployed at 10m on both equatorial Pacific 

moorings (0° 155°W and 2°S 170°W) to supplement the discrete wavelengths measured at 20 m. These 

configuration changes yielded data of significantly higher quality for almost the same cost as the existing

discrete-wavelength instruments. These instruments were retrieved in June 2003 (new HR3 units were 

subsequently deployed after recovery) providing hyperspectral data consisting of:

• downwelling irradiance above the surface (3m)

• downwelling irradiance and upwelling radiance at 10m depth. 

The Hydrorad data are available in programmable bin sizes (highest resolution 0.37nm), over the range 

~300 to 850nm, but for deployment in the equatorial Pacific we have binned the data to ~2nm resolution. 

We also successfully recovered and deployed HOBILabs Hydroscat 2 (HS2) instruments fitted with new 

copper anti-fouling shutters. The HS2 measures backscatter and fluorescence at two wavelengths (490 and 

676 nm). With the deployment of these new instruments, particularly the hyperspectral radiometers, we 

have been able to submit optical data beyond our initial deliverables as stated in the SIMBIOS grant, and 

can better support recent and forthcoming ocean color missions such as MODIS and GLI and the 

development of ocean color algorithms that will go beyond chlorophyll. 

Optical profiling measurements 

On mooring maintenance cruises, optical profiles of the euphotic zone are performed daily, when 

possible, close to local noon using a 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 are obtained. 

Products of interest (mostly Lwn) are provided to NASA post-cruise, and the profile data are archived at

MBARI along with existing optical profiles from almost every oceanic region. 

In situ measurements 

Table 4.1 recaps the cruises embarked on by MBARI this fiscal year in support of SIMBIOS. The 

cruise-based measurements consist primarily of fluorometric chlorophyll (Chavez et al. 1995) & 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. Latitude-depth sections of shipboard data can now be viewed at:http://www.mbari.org/~ryjo/tropac/ 

sections 

On mooring maintenance cruises (the 155°W and 170°W meridional transects), HPLC samples are 

collected and productivity (14C, 15N) measurements are also performed. These data are stored at MBARI 

and the pigment data supplied to the SeaBASS database for algorithm development. Figure 4.1 shows a 

42 
SIMBIOS Project Annual Report 

regression comparison, with good agreement between shipboard measurements and HPLC analysis of 

chlorophyll a. The HPLC analysis was conducted by the Center for Hydor-Optics and Remote Sensing 

(CHORS). 

y = 1.048x + 0.048

r

2

 = 0.97

n = 717

0

2

4

6

8

10

12

14

0 2 468 10 12

MBARI Fluorometer (mg m

-3)

CHOR Fluorometer (mg m-3)

14

. 

Figure 4.1: Regression model comparing in situ Chlorophyll a determined by a Turner fluorometer at sea 

versus chlorophyll determined by HPLC at the Center of Hydro-Optics and Remote Sensing (CHORS). 

Samples were collected from both the equatorial Pacific and Monterey Bay.

4.3 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 biological-physical coupling observed in the central equatorial Pacific during the start 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 complete picture of the ecosystem’s reaction to the intense physical forcings that 

occurred during the 1997-98 El Niño. Strutton and Chavez (2000) 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. (2001) used time series from smaller Satlantic bio-optical packages and SeaWiFS imagery to detail

the extreme anomalies in chlorophyll related to the passage of tropical instability waves (TIWs) during the 

latter half of 1998 and 1999. These data not only documented the magnitude of the observed chlorophyll

variability, but also shed light on the mechanisms possibly responsible for the concentration of chlorophyll 

in connection with TIWs. Ryan et al. (2002) thoroughly described and explained the intense and 

widespread blooms that dominated the equatorial Pacific during the 1998 La Niña. 

Chavez et al. (2000) documented the design, and demonstrated the effectiveness of a shutter 

mechanism that prevents bio-fouling of the in situ radiometers on the moorings. Kuwahara et al. (2003) 

contributed to the 4th revision of the Ocean Optics Protocols series with a chapter titled, “Radiometric and 

Bio-optical Measurements from Moored and Drifting Buoys: Measurement and Data Analysis Protocols”.

43 
SIMBIOS Project Annual Report 

Other manuscripts currently in press/preparation include a chapter in Seuront and Strutton (Eds):

‘Handbook of scaling methods in aquatic ecology: Measurements, analysis, simulation’ (CRC Press, 

publication October 2003), a manuscript describing the biological component of the equatorial Pacific heat

budget (Strutton and Chavez, in press), and a paper that analyzes the time series of optical data collected at

the mooring location (Kuwahara et al.)

0.0

0.5

1.0

1.5

2.0

2.5

1996 1997 1998 1999 2000 2001 2002 2003

Lwn412

Lwn443

Lwn490

Lwn510

Lwn555

0.0

0.5

1.0

1.5

2.0

2.5

1996 1997 1998 1999 2000 2001 2002 2003

Lwn412

Lwn443

Lwn490

Lwn510

Lwn555

EP1

EP2

0.0

0.5

1.0

1.5

2.0

2.5

1996 1997 1998 1999 2000 2001 2002 2003

Lwn412

Lwn443

Lwn490

Lwn510

Lwn555

0.0

0.5

1.0

1.5

2.0

2.5

1996 1997 1998 1999 2000 2001 2002 2003

Lwn412

Lwn443

Lwn490

Lwn510

Lwn555

0.0

0.5

1.0

1.5

2.0

2.5

1996 1997 1998 1999 2000 2001 2002 2003

Lwn412

Lwn443

Lwn490

Lwn510

Lwn555

0.0

0.5

1.0

1.5

2.0

2.5

1996 1997 1998 1999 2000 2001 2002 2003

Lwn412

Lwn443

Lwn490

Lwn510

Lwn555

EP1

EP2

Figure 4.2: The time series (December 11,1996 – June 18, 2003) of normalized water-leaving radiance, Lwn 

[μW cm-2 nm-1 sr-1], derived from the Lu and Kd data obtained from MBARI optical instruments on the 

TAO moorings at 0° 155°W (EP1) and 2°S 170°W (EP2). The data have been subject to quality control as 

described in the methods. 

SeaWiFS Calibration/Validation 

At the present time, we are concentrating on refining our data analysis and quality control protocols of

the full mooring records (1996-2003) for both moorings in combination with the newly reprocessed 

SeaWiFS data. Analysis and improvements to quality control methods continue on the reprocessed data. 

These data have been uploaded to SeaBASS. Figure 4.2 shows our most recent update of the time series of 

Lwn. Note: Lwn collected from the upwelling region is more variable than data collected from EP2, which is 

less susceptible to upwelling.

McClain and Fargion (1999b) showed matchup data derived from optical profiles of the SPMR in the 

equatorial Pacific. The mooring data collected at EP1 (excluding the anomalous El Nino year) indicated 

good agreement between the satellite- and profile-derived water-leaving radiance values (Figure 4.3). 

Similarly, Figure 4.4 compares the surface chlorophyll values obtained from SPMR casts with the extracted 

chlorophyll samples obtained at the same location. 

In addition to our deliverables, the mooring data collected at EP1 during 2002 yielded hyperspectral Ed

data (Figure 4.5) and backscattering data, namely bb(490) and bb(676) (Figure 4.6). 

44 
SIMBIOS Project Annual Report 

0.00

0.25

0.50

0.75

1.00

1.25

400 425 450 475 500 525 550 575 600

SeaWifs

EP1

SPMR

Wavelength (nm)

Lw (

m

W cm-2 nm-1 sr-1)

0.00

0.25

0.50

0.75

1.00

1.25

400 425 450 475 500 525 550 575 600

SeaWifs

EP1

SPMR

Wavelength (nm)

Lw (

m

W cm-2 nm-1 sr-1)

Figure 4.3: Water leaving radiance matchups between SeaWiFS, EP1 (0, 155W) optical mooring, and 

SPMR profiles (where available) from June 1998 to Oct 2001. Anomalous data collected during the 1997 – 

1998 El Nino period were not included. The number of matching points between SeaWiFS and the optical

mooring at 412, 443, 490, 510, and 555 nm were, n = 245, 238, 236, 208 and 210, respectively. For SPMR

profiles conducted at the mooring location, n = 10. 

A

y = 0.93x + 0.01

R2

 = 0.64

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6

in situ & SeaWiFS Chl (μg L-1)

Mooring Chl ( μg L-1)

Mooring CHL

Shipboard CHL

B

y = 0.62x + 0.05

R2

 = 0.80

0.0

0.1

0.2

0.3

0.4

0.0 0.1 0.2 0.3 0.4

in situ Chl (μg L-1)

S

P

MR Chl ( μg L-1)

SPMR CHL

Figure 4.4: (A) Comparison of SeaWiFS and shipboard in situ chlorophyll with mooring-derived 

chlorophyll for the MBARI instruments at 0° 155°W (EP1). Mooring-derived chlorophyll was calculated 

using an algorithm by Morel (1988). Regression analysis for SeaWiFS vs. Mooring Chl is also shown. (B) 

Comparison of chlorophyll from shipboard water samples with chlorophyll calculated from SPMR casts. 

SPMR chlorophyll is calculated using OC4V4 algorithm by O’Reilly (2001) applied to normalized waterleaving radiance from the SPMR. All data have been subject to quality control. 

45 
SIMBIOS Project Annual Report 

Figure 4.5: Time series of hyperspectral downwelling irradiance at EP1. Data of this kind could be used in

further algorithm development or as validation data for future ocean color missions. 

Figure 4.6: In situ backscatter data between October 2002 and June 2003, measured with a HOBI Labs, 

Inc. HS-2 illustrates the variability in particle density at EP1. At times this variability can also be seen in

the Rrs data (dotted line, Rrs(510) shown as an example) derived from mooring data.

46 
SIMBIOS Project Annual Report 

4.4 CONCLUSIONS 

Aside from setbacks related to vandalism (mainly tower and instrument controller theft), the two major 

mooring installations at 0°, 155°W and 2°S, 170°W are operating relatively well. The deployment of 

hyperspectral and backscatter instruments at both moorings is demonstrating significant potential and will 

better support validation efforts for ocean color missions. 

This year alone we have preformed ~30 SPMR profiles, analyzed more than 2500 shipboard 

chlorophyll measurements, processed over 500 nutrient samples, processed 200 a* samples and collected 

close to 1000 HPLC samples. The program of cruise-based measurements as described above will continue 

on eight equatorial Pacific cruises during 2003-2004, with scheduled SeaWiFS LAC where applicable. 

Table 4.1: Summary of cruises (in 2003) 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 GP6-02-RB aboard the 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, and every 0.5° between 3°N and 3°S. Measurements consisted of chlorophyll (Chl) plus nitrate, 

nitrite, phosphate and silicate (Nutrients) at 8 depths between 0 and 200m. On selected cruises, primary 

production (PP) and new production (NP) measurements were also made using 14C and 15N incubation 

techniques, respectively. Daily optical profiles were obtained using the Satlantic SeaWiFS Profiling 

Multispectral Radiometer (SPMR) where indicated. 

Cruise ID Dates Meridional transects Measurements 

GP2-03-KA 24-Mar-03 to 28-Apr-03 95W and 110W Chl, Nutrients, a* 

GP3-03-KA 05-Jun-03 to 11-Jul-03 155W and 170W Chl, NP, PP, Nutrients, SPMR, a*, HPLC

GP4-03-KA 13-Jul-03 to 11-Aug-03 165E and 180W Chl, Nutrients, a* 

GP5-03-KA 21-Aug-03 to 27-Sep-03 125W and 140W Chl, Nutrients, a* 

GP6-03-RB 11-Oct-03 to 12-Nov-03 95W and 110W Chl, Nutrients, a* 

GP7-03-KA 16-Oct-03 to 14-Nov-03 155W and 170W Chl, NP, PP, Nutrients, SPMR, a*, HPLC

GP8-03-KA 16-Nov-03 to 14-Dec-03 165E and 180W Chl, Nutrients, a* 

REFERENCES 

Chavez, F.P., and Barber, R.T., 1987: An estimate of new production in the equatorial Pacific, Deep-Sea 

Research, 34, 1229-1243. 

Chavez, F.P., Buck, K.R.., Bidigare, R.R., Karl, D.M., Hebel, D., Latasa, M., Campbell, L. and Newton, J., 

1995: On the chlorophyll a retention properties of glass-fiber GF/F filters. Limnology and 

Oceanography, 40, 428-433. 

Chavez, F.P. and Toggweiler, J.R., 1995: Physical estimates of global new production: the upwelling 

contribution, in Upwelling in the Ocean: Modern Processes and Ancient Records C. P. Summerhayes, 

K. C. Emeis, M. V. Angel, R. L. Smith, B. Zeitzschel, Eds., J. Wiley & Sons, Chichester, pp. 313-320. 

Chavez, F.P., Buck, K.R., Service, S.K., Newton, J. and Barber, R.T., 1996: Phytoplankton variability in 

the central and eastern tropical Pacific. Deep-Sea Research, 43(4-6), 835-870. 

Chavez, F.P., Strutton, P.G., and McPhaden, M.J., 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. 

47 
SIMBIOS Project Annual Report 

Chavez, F.P., Strutton, P.G., Friederich, G.E., Feely, R.A., Feldman, G., Foley, D. 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., 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.

Dugdale, R.C. and Wilkerson, F.P., 1998: Silicate regulation of new production in the equatorial Pacific 

upwelling, Nature, 391, 270-273. 

Feely, R.A., Wanninkhof, R., Takahashi, T. and Tans, P., 1999: Influence of El Niño on the equatorial

Pacific contribution to atmospheric CO2 accumulation. Nature, 398, 597-601. 

Frouin, R., Ligner, D.W., and Gautier, C., 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. 

Kuwahara, V. S., Strutton, P.G., Dickey, T.D., Abbott, M.R., Letelier, R.M., Lewis, M.R., McLean, S., 

Chavez, F.P., Barnard, A., R. Morrison, J.R., Subramaniam, A., Manov, D., Zheng, X. and Mueller, 

J.L., 2003: Radiometric and Bio-optical Measurements from Moored and Drifting Buoys:

Measurement and Data Analysis Protocols. NASA Tech. Memo, Eds. J.L. Muller and G.S. Fargion, 

NASA Goddard Space Flight Center, Goddard, Maryland, Volume VI. 

McClain C.R. and Fargion, G.S., 1999a: SIMBIOS Project 1998 Annual Report. NASA Tech. Memo. 1999-

208645, NASA Goddard Space Flight Center, Greenbelt, Maryland, 105pp. 

McClain C.R. and Fargion, G.S., 1999b: SIMBIOS Project 1999 Annual Report. NASA Tech. Memo. 1999-

209486, NASA Goddard Space Flight Center, Greenbelt, 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. 

O'Reilly, J. E., Maritorena, S., O’Brien, M.C., Siegel, D.A., Toole, D., Menzies, D., Smith, R.C., Mueller, 

J.L., Mitchell, B.G., Kahru, M., Chavez, F.P., Strutton, P.G., Cota, G.F., Hooker, S.B., McClain, C.R., 

Carder, K.L., Muller-Karger, F., Harding, L., Magnuson, A., Phinney, D., Moore, G.F., Aiken, J., 

Arrigo, K.R., Letelier, R.M. and M. Culver, 2001: SeaWiFS Postlaunch Calibration and Validation 

Analyses, Part 3, SeaWiFS Postlaunch Technical Report Series, Volume 11, NASA Tech. Memo. 2000-

206892, S.B. Hooker and E.R. Firestone, Eds., NASA Goddard Space Flight Center, Greenbelt, 

Maryland. 

Strutton, P.G and Chavez, F.P. Radiant heating in the equatorial Pacific: Spatial and Temporal variability. 

Journal of Climate, in press. 

Ryan, J.P., Polito, P.S., Strutton, P.G. and Chavez, F.P. Anomalous phytoplankton blooms in the equatorial 

Pacific. Progress in Oceanography, 55, 263-285. 

Strutton, P.G., Ryan, J.P. and Chavez, F.P., 2001: Enhanced chlorophyll associated with tropical instability 

waves in the equatorial Pacific. Geophysical Research Letters, 28(10), 2005-2008. 

Strutton, P.G. and Chavez, F.P., 2000: Primary productivity in the equatorial Pacific during the 1997-98 El

Niño. Journal of Geophyiscal Research, 105(C11), 26,089-26,101. 

48 
SIMBIOS Project Annual Report 

Strutton, P.G. and Chavez, F.P., 2003: Scales of biological-physical coupling in the equatorial Pacific. In 

Handbook of scaling methods in aquatic ecology: Measurement, analysis simulation, Seuront, L. and 

Strutton, P.G. (Eds) CRC Press, Boca Raton, FL. 

Tans, P.P., Fung, I.Y., Takahashi, T., 1990: Observational constraints on the global atmospheric CO2

budget, Science, 247, 1431-1438. 

Takahashi, T, Feely, R.A., Weiss, R.F., Wanninkhof, R., Chipman, D.W., Sutherland, S.C. and Takahashi,

T., 1997: Carbon dioxide and climate change. Proceedings of the National Academy of Sciences, 

94, 8314-8319. 

This Research was Supported by 

the NASA Contract # 00193 

Publications 

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. & 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, 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. 

Cosca, C.E., Feely, R.A., Boutin, J., Etcheto, J., McPhaden, M.J., Chavez, F.P. and Strutton, P.G., 2003. 

Seasonal and interannual CO2 fluxes for the central and eastern equatorial Pacific Ocean as determined 

by fCO2-SST relationships. Journal of Geophysical Research, 108(C8), doi: 10.1029/ 2000JC000677. 

Frouin, R., Franz, B., Wang, M., Werdell, P.J., Bishop J.K.B., Chavez, F., and Gower, J.F. Algorithm to

estimate photosynthetically available radiation at the ocean surface from SeaWiFS data, in preparation. 

Johnson, K.S. and Coletti, L.J., 2002. In situ ultraviolet spectrophotometry for high resolution and longterm monitoring of nitrate, bromide and bisulfide in the ocean. Deep-Sea Research I, 49, 1291–1305. 

Ryan, J.P., Polito, P.S., Strutton, P.G. and Chavez, F.P., 2002. Anomalous phytoplankton blooms in the 

equatorial Pacific. Progress in Oceanography, 55, 263-285. 

Sabine, C.L., Feely, R.A., Johnson, G.C., Strutton, P.G., Lamb, M.F. and McTaggart, K.E. Carbon 

chemistry of the water column during the GasEx-2001 experiment. Journal of Geophysical Research, 

submitted.

Schwarz, J.N., Gege, P., Kowalczuk, P., Kaczmarek, S., Carder, K.L., Muller-Karger, F., Varela,R., 

Chavez, F.P., Clark, D.K., Cota, G.F., Cunningham, A., McKee, D., Kishino, M., Mitchell, B.G., 

Phinney, D.A., Raine, R., Trees, C.C., Zaneveld, J.R.V., Pegau, S. Two models for Gelbstoff 

absorption. Oceanologia, in revision. 

Strutton, P.G. and Chavez, F.P., 2000. Primary productivity in the equatorial Pacific during the 1997-98 El

Niño. Journal of Geophyiscal Research, 105(C11), 26,089-26,101. 

Strutton, P.G., Ryan, J.P. and Chavez, F.P., 2001. Enhanced chlorophyll associated with tropical instability 

waves in the equatorial Pacific. Geophysical Research Letters, 28(10), 2005-2008. 

49 
SIMBIOS Project Annual Report 

Strutton, P.G. and Chavez, F.P., 2003. Scales of biological-physical coupling in the equatorial Pacific, in

Handbook of scaling methods in aquatic ecology: Measurment, analysis, simulation., Seuront, L. and 

Strutton, P.G., Eds, CRC Press, Boca Raton, FL.

Strutton, P.G and Chavez, F.P. Radiant heating in the equatorial Pacific: Observed variability and potential 

for real-time calculation. Journal of Climate, in press. 

Strutton, P.G., Chavez, F.P., Dugdale, R.C. and Hogue, V. Primary productivity and its impact on the 

carbon budget of the upper ocean during GasEx-2001. Journal of Geophysical Research, accepted 

pending revision.

NASA Technical Memorandum 

Kuwahara, V.S., Strutton, P.G., Subramaniam, A., Dickey, T.D., Abbot, M.R., Letelier, R., Lewis, M.R., 

McLean, S., Barnard, A., Ru Morrison, J. and Chavez, F.P. Radiometric and Bio-optical Measurements 

from Moored and Drifting Buoys: Measurement and Data Analysis Protocols, Chapter VI-3, Ocean 

Optics Protocols for Satellite Ocean Color Sensor Validation, Volume 4, in press. 

O'Reilly, J. E., Maritorena, S., O’Brien, M.C., Siegel, D.A., Toole, D., Menzies, D., Smith, R.C., Mueller, 

J.L., Mitchell, B.G., Kahru, M., Chavez, F.P., Strutton, P.G., Cota, G.F., Hooker, S.B., McClain, C.R., 

Carder, K.L., Muller-Karger, F., Harding, L., Magnuson, A., Phinney, D., Moore, G.F., Aiken, J., 

Arrigo, K.R., Letelier, R.M. and Culver, M. SeaWiFS Postlaunch Calibration and Validation Analyses, 

Part 3, SeaWiFS Postlaunch Technical Report Series, Volume 11, NASA Tech. Memo. 2000-206892, 

S.B. Hooker and E.R. Firestone, Eds., NASA Goddard Space Flight Center, Greenbelt, Maryland, 

2001. 

Presentations 

Chavez, F.P., Feldman, G.C., McPhaden, M.J., Foley, D.G. and Strutton, P.G., 1998: Remote Sensing of 

the Equatorial Pacific during 1997-98. AGU Fall Meeting, San Francisco, CA, December, Invited 

Friederich, G.E., Chavez, F.P., Strutton, P.G. and Walz, P., 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, 

TX. 

Kuwahara, V.K., Strutton, P.G., Penta, B., Drake, E., and Chavez, F.P., 2001: Bio-optics from Moorings:

Satellite Validation and Ecosystem Dynamics. AGU Fall Meeting, San Francisco, CA, December. 

Strutton, P.G., Poster presentation, AGU, Fall Meeting, San Francisco, December 2002. 

Strutton, P.G., Poster presentation and session chair, Ocean Optics XVI meeting, Santa Fe, November 

2002. 

Strutton, P.G., Oral presentation, American Geophysical Union / American Society of Limnology and 

Oceanography, Ocean Sciences Meeting, Honolulu, February 2002. 

Strutton, P.G., Chavez, F.P., Drake, E., Kuwahara, V.K, Friederich, G. and Ryan, J., 2001: Merging Data

from Ships, Satellites and Moorings to Understand Biological-Physical Coupling in the Equatorial 

Pacific. AGU Fall Meeting, San Francisco, CA, December 2001. 

50 
SIMBIOS Project Annual Report 

Strutton, P.G., Chavez, F.P., Foley, D.G., Sclining, B.M. and McPhaden, M.J., 1999: Remote Sensing of 

Biological-Physical Coupling in the Equatorial Pacific. ASLO Ocean Sciences Meeting, Santa Fe, NM. 

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., Chavez, F.P. and McPhaden, M.J., 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, 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, P.G., Chavez, F.P. and McPhaden, M.J., 1998: Biological-physical coupling in the central

equatorial Pacific during the 1997-98 El Niño, Ocean Optics XIV, Kailua-Kona, HI 

51 
SIMBIOS Project Annual Report 

Chapter 5 

Satellite Ocean-Color Validation Using Ships of 

Opportunity 

Robert Frouin, David L. Cutchin, Lydwine Gross-Colzy and Antoine Poteau 

Scripps Institution of Oceanography, University of California San Diego, La Jolla 

Pierre-Yves Deschamps 

Laboratoire d’Optique Atmosphérique, Université des Sciences et Technologie de Lille, 

France 

5.1 INTRODUCTION 

The investigation’s main objective is to collect from platforms of opportunity (merchant ships, 

research vessels) concomitant normalized water-leaving radiance and aerosol optical thickness data over 

the world’s oceans. A global, long-term data set of these variables is needed to verify whether satellite 

retrievals of normalized water-leaving radiance are within acceptable error limits and, eventually, to adjust 

atmospheric correction schemes. 

To achieve this objective, volunteer officers, technicians, and scientists onboard the selected ships 

collect data from portable SIMBAD and Advanced SIMBAD (SIMBADA) radiometers. These instruments 

are specifically designed for evaluation of satellite-derived ocean color. They measure radiance in spectral 

bands typical of ocean-color sensors. The SIMBAD version measures in 5 spectral bands centered at 443, 

490, 560, 670, and 870 nm, and the Advanced SIMBAD version in 11 spectral bands centered at 350, 380, 

412, 443, 490, 510, 565, 620, 670, 750, and 870 nm. Aerosol optical thickness is obtained by viewing the 

sun disk like a classic sun photometer. Normalized water-leaving radiance, or marine reflectance, is

obtained by viewing the ocean surface through a vertical polarizer in a specific geometry (nadir angle of 

45o

 and relative azimuth angle of 135o) to minimize direct sun glint and reflected sky radiation. The 

SIMBAD and SIMBADA data, after proper quality control and processing, are delivered to the SIMBIOS 

project office for inclusion in the SeaBASS archive. They complement data collected in a similar way by 

the Laboratoire d’Optique Atmosphérique of the University of Lille, France. 

The SIMBAD and SIMBADA data are used to check the radiometric calibration of satellite oceancolor sensors after launch and to evaluate derived ocean-color variables (i.e., normalized water-leaving 

radiance, aerosol optical thickness, and aerosol type). Analysis of the SIMBAD and SIMBADA data 

provides information on the accuracy of satellite retrievals of normalized water-leaving radiance, an 

understanding of the discrepancies between satellite and in situ data, and algorithms that reduce the 

discrepancies, contributing to more accurate and consistent global ocean color data sets. 

5.2 RESEARCH ACTIVITIES 

Since November 2002, SIMBAD measurements were made during 7 research cruises of opportunity, 

bringing to 69 the number of campaigns with SIMBAD measurements realized during the period October 

1996-Otober 2003. These 7 cruises, as well as ongoing and planned cruises, are listed in Table 1 with name

of cruise, SIMBAD and SIMBADA instrument(s) used, region of measurements, name of operator, and 

dates of measurements. The data were collected In the Pacific Ocean, off the West Coast of the United 

States and Baja California, Mexico (CalCOFI and IMECOCAL cruises), between Peru and New Zealand 

(P500304), and between Australia and Papeete (BEAGLE2003, Leg1). The location of the measurements

from the first 5 cruises, which have been processed, is displayed in Figure 5.1. The processed data (a total

of 606 complete data sets) have been transferred to the SeaBASS archive. 

52 
SIMBIOS Project Annual Report 

 Figure 5.1: Geographic location of the SIMBAD data sets acquired since November 1, 2002. 

 1 2 3 4 5 6

 7 8 9 10 11 12

13 14 15 16 17 18

19 20 21 22 23 24

25 26 27 28 29 30

0.1 1 10 0

0.1

0.2

r (μm)

dV / dln r

31 32 33 34 35 36

Figure 5.2: The calibrated 36 PRSOM neurons: Volume size distribution dV/dLnr. In each 

plot, the data gathered by the corresponding neuron is plotted in black, while their referent

vector is plotted in gray. 

53 
SIMBIOS Project Annual Report 

 1 2 3 4 5 6

 7 8 9 10 11 12

13 14 15 16 17 18

19 20 21 22 23 24

25 26 27 28 29 30

441 640 8401022 1.3

1.4

1.5

1.6

λ (nm)

n(

λ)

31 32 33 34 35 36

Figure 5.3: Same as Figure 5.2, but real part n of the refractive index 

 1 2 3 4 5 6

 7 8 9 10 11 12

13 14 15 16 17 18

19 20 21 22 23 24

25 26 27 28 29 30

441 640 8401022

0.001

0.01

0.1

λ (nm)

k(

λ)

31 32 33 34 35 36

Figure 5.4: Same as Figure 5.2, but imaginary part k of the refractive index, plotted on a logarithmic scale.

54 
SIMBIOS Project Annual Report 

Table 5.1. SIMBAD cruises during November 2002 –October 2003. 

–IMECOCAL 0210/11,Ensenada-San Carlos, B/O Francisco de Ulloa, Simbad08, Jushiro Cepeda, 16 Oct - 06 Nov 02 

–IMECOCAL 0301/02, Baja California, B/O Francisco de Ulloa, Simbad08, Jushiro Cepeda, 08 Feb - 15 Feb 03 

–CalCOFI0304, Southern California Bight, R/V Roger Revelle , Simbad03, Haili Wang, 03 Apr - 27 Apr 03 

–IMECOCAL 0304/05, Baja California, B/O Francisco de Ulloa, Simbad06, Jushiro Cepeda, 08 Apr - 22 Apr 03 

–P500304, Southern Pacific, M/V Nacre, Simbad 07 David Cutchin, 24 Apr - 5 May 03 

–IMECOCAL 0307, Baja California, B/O Francisco de Ulloa, Simbad08, Martin de la Cruz, 01 Jul - 22 Jul 03 

–BEAGLE2003, Leg1, Western Pacific, R/V Mirai, Simbad 03/07, SimbadA01, Brian Irvin, 03 Aug - 06 Sep 03 

–BEAGLE2003, Leg2, Eastern Pacific, R/V Mirai, Simbad 03/07, SimbadA01, Gadiel Alarcon, 09 Sep - 16 Oct 03 

–IMECOCAL 0310, Baja California, B/O Francisco de Ulloa, Simbad08, Martin de la Cruz, 10 Oct - 31 Oct 03 

–BEAGLE2003, Leg3, Valparaiso-Santos, R/V Mirai, Simbad 03/07, SimbadA01, Vivian Lutz, 19 Oct - 02 Nov 03 

–OAS1003, Western South Atlantic, R/V Aldebaran, Simbad06, SimbadA02, Denise Vizziano, 26 Oct – 04 Nov 03 

The history of SIMBAD calibration coefficients and the accuracy of the view angles measured by the 

radiometers have been analyzed. This effort started during the first year of the investigation, but was 

continued the second and third years with additional data. Trends in the calibration coefficients and biases 

in the view angles were removed, and all the SIMBAD data acquired since October 1996 have been reprocessed or processed with adjusted calibration coefficients. Several aspects of satellite ocean-color 

remote sensing have been examined. They include the selection of aerosol models for atmospheric 

correction, the SeaWiFS performance in varied oceanic regions, a new algorithm to retrieve marine 

reflectance and chlorophyll-a concentration based on principal component analysis of atmospheric effects, 

and the SeaWiFS radiometric calibration in the near infrared. An inversion scheme was also developed to

retrieve aerosol scale height from POLDER and MERIS data. The main results and findings are 

summarized in the next section (see also, below, the list of publications since 2002). 

5.3. RESEARCH RESULTS 

Aerosol mixtures for ocean color remote sensing 

A Probabilistic Self-Organizing Map (PRSOM) has been applied to AERONET retrievals of aerosol

size distribution and refractive index at island and coastal sites (Figures 5.2, 5.3, and 5.4). The PRSOM

suggests that there are two strong (likely) situations: weakly absorbing mixtures on the one hand, and 

absorbing dust or urban soot aerosols on the other hand. Intermediate situations are possible, but not

probable. The weakly absorbing mixtures (Figure 5.5) are very different from the classical (Shettle and

Fenn, 1979) aerosol models (Figures 5.6 and 5.7). If we assume that AERONET provides representative 

conditions for ocean color remote sensing, two important results have to be emphasized. First, the classical 

models allow one to process low values of the Angström exponent α (670), but these conditions may not be 

the most encountered, even in the open ocean. Second, the PRSOM mixtures indicate a different 

association between Angström exponent and single scatttering albedo. As expected, it is not possible to 

separate absorbing from weakly absorbing mixtures from a given (observed) ε (χ, 670, 865) (Figures 5.8 

and 5.9). However, within one of the two types of situations, the PRSOM neurons can be clearly identified

using a directional sampling of the ε parameter (case of POLDER & MISR). Figure 5.9 summarizes the 

directional behavior of ε using a principal component analysis. Three principal components restore 99% of 

the information The principal components of the weakly absorbing mixtures have the same range of 

variability as those of the absorbing mixtures (Figure 5.9b).

55 
SIMBIOS Project Annual Report 

0

0.05

0.1

 0.47

α(670)

1.3

1.4

1.5

0

1

2

x 10−3

0

0.05

0.1

 0.71

1.3

1.4

1.5

0

1

2

x 10−3

0

0.05

0.1

 1.00

1.3

1.4

1.5

0

1

2

x 10−3

0

0.05

0.1

 1.31

1.3

1.4

1.5

0

1

2

x 10−3

0

0.05

0.1

 1.66

1.3

1.4

1.5

0

1

2

x 10−3

0

0.05

0.1

 2.00

1.3

1.4

1.5

0

1

2

x 10−3

0

0.05

0.1

 2.13

1.3

1.4

1.5

0

1

2

x 10−3

0.1 1 10

0

0.05

0.1

 2.14

r (μm)

dV/dln r

441 640 8401022

1.3

1.4

1.5

λ (nm)

n(

λ)

441 640 8401022

0

1

2

x 10−3

λ (nm)

k(

λ)

Figure 5.5: Gravity centers of the eight bins dividing the “open ocean” ensemble. First column is dV/dLnr, 

second column is n, and third column is k. In each plot of dV/dLnr, the corresponding Angström exponent, 

α(670) is indicated in a framed box. 

56 
SIMBIOS Project Annual Report 

60 90 120 150 180

0.1

1

(a)

χ ( o )

P(

χ,865)

S−Models

60 90 120 150 180

0.1

1

(b)

χ ( o )

P(

χ,865)

A−Models

412 555 670 765 865

0.92

0.93

0.94

0.95

0.96

0.97

0.98

0.99

1

(c)

λ (nm)

ω(λ)

412 555 670 765 865

0.92

0.93

0.94

0.95

0.96

0.97

0.98

0.99

1

(d)

λ (nm)

ω(λ)

Figure 5.6: Phase function and single scattering albedo of the Standard (S-) models, 

(a) and (c), and of the AERONET (A-) models, (b) and (d). 

443 555 670 765 865

1

2

3

S−Models

χ = 150o

(a)

λ (nm)

ε (

χ,λ,865)

443 555 670 765 865

1

2

3

A−Models

χ = 150o

(b)

λ (nm)

ε (

χ,λ,865)

443 555 670 765 865

1

1.5

2

2.5

3

χ = 120o

(c)

λ (nm)

ε (

χ,λ,865)

443 555 670 765 865

1

1.5

2

2.5

3

χ = 120o

(d)

λ (nm)

ε (

χ,λ,865)

443 555 670 765 865

0.5

1

1.5

2

2.5

3

χ = 90o

(e)

λ (nm)

ε (

χ,λ,865)

443 555 670 765 865

0.5

1

1.5

2

2.5

3

χ = 90o

(f)

λ (nm)

ε (

χ,λ,865)

Figure 5.7: Spectral behavior of ε for the two sets of aerosol models and three 

scattering angles, χ. 

57 
SIMBIOS Project Annual Report 

1.1 1.2 1.3 1.4 1.5

0

1

2

3

4

5

6

Distribution

(a)

ε

j

(120,670,865)

1.1 1.2 1.3 1.4 1.5

0

1

2

3

4

5

6

Distribution

(b)

ε

j

(120,670,865)

Figure 5.8: Histogram of e(c = 120, 670, 865) for the weakly absorbing neurons (a) and the absorbing 

neurons (b). 

1 2

3

5 6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

24 23

25

26

27

28

29

30

31

32 33

34

35

36

PC 1

PC 2

(b)

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

0

0.2

0.4

0.6

0.8

1

PC 3

0

0.2

0.4

0.6

0.8

1

60 80 100 120 140 160 180

−0.2

−0.15

−0.1

−0.05

0

0.05

0.1

0.15

0.2

χ (o)

eigen vectors

(a) e1

e2

e3

Figure 5.9: The results of a principal component analysis performed on the PRSOM neurons. (a) The first,

second, anf third eigen vectors of the ε(χ, 670, 865) covariance matrix, for c ranging from 60 to 180o

. (b) 

Three-dimensional plot of the first three principal components (PC) of the PRSOM neurons (encoded 

between 0 and 1 for commodity). The third PC (PC3) is displayed using a color scale. Small circles denote 

absorbing neurons and large circles weakly absorbing neurons. The neuron number is indicated in the 

vicinity of its coordinates. 

58 
SIMBIOS Project Annual Report 

Figure 5.10: SeaWiFS-derived and measured marine reflectance at match-up stations during ACE-Asia. 

Marine reflectance from SeaWiFS is generally underestimated, especially in the blue. 

Thus the two types of mixtures cannot be distinguished. However, within the absorbing mixtures, the 

dust neurons are easily separated from the urban/soot neurons. In an operational situation, the sampling of 

the χ space may vary from a pixel to another, but a scalar product between the measured ε and the eigen 

vectors (displayed in Figure 5.9a) can still be computed, even over a restricted χ range. A simple study

made for a POLDER orbit shows that the PRSOM weakly absorbing mixtures can be always separated by

computation of their principal components, which may be used in an ocean color processing line. Details 

about the study can be found in Gross et al., (2003) and Gross-Colzy and Frouin (2003). 

Evaluation of SeaWiFS-derived marine reflectance and chlorophyll concentration 

Marine reflectance, chlorophyll-a concentration, and particulate back-scattering coefficient derived 

from SeaWiFS imagery have been evaluated in various oceanic regions and atmospheric environments. 

These include East Asian Seas (Li et al., 2003), the Black Sea and the Eastern Mediterranean Sea (Sancak 

et al., 2003), and various bio-provinces of the Atlantic Ocean (Frouin et al., 2003). 

Figures 5.10 and 5.11 display retrieved and measured marine reflectance and aerosol optical thickness, 

respectively, at match-up stations during ACE-Asia (R/V Brown cruise). The SeaWiFS marine reflectance 

is generally lower than the measurements, especially in the blue. The SeaWiFS aerosol optical thickness at 

865 nm is larger than the measurements, but its spectral dependence is smaller. Despite the discrepancies, 

retrieved and measured chlorophyll-a concentrations are in fairly good agreement, with a correlation 

coefficient squared of 0.78 and a rms difference of 62%. The lower marine reflectance in the blue may be 

59 
SIMBIOS Project Annual Report 

attributed to absorbing aerosols, not only dust, but also sub-micron particles (soot from diesel fuel and coal

consumption), as suggested by Li et al. (2003). 

Figure 5.11: SeaWiFS-derived and measured aerosol optical thickness at match-up stations during ACEAsia. Optical thickness from SeaWiFS is often larger at 865 nm, but its spectral dependence is smaller. 

Along Atlantic transects in October-November 2001 and March-April 2002 (R/V Ioffe cruise), 

SeaWiFS and HPLC chlorophyll-a concentrations agree qualitatively, but there is overestimation by the 

SeaWiFS algorithm in the oligotrophic waters of both transects (Frouin et al., 2003). Changes in the 

absolute values and in the form of the spectral absorption of the particulate matter are observed in the 

waters of different productivity sampled (Fig. 5.12), and such features are important in the development of 

regional bio-optical algorithms. A general resemblance is noted between particulate back-scattering and 

chlorophyll distributions. This is not surprising, because back-scattering coefficient depends on particulate 

matter in seawater that can originate from phytoplankton as a primary source. But the particulate backscattering distributions (Fig. 5.13) also bear a similarity to the aerosol optical thickness distributions in the 

central Atlantic (Fig. 5.14), where high values due to transport of Saharan dust occur. 

The performance of the OC2 and OC4 algorithms to estimate chlorophyll-a concentrations has also 

been tested in two contrasted bio-optical environments, the Black Sea and the Mediterranean Sea (Sancak 

et al., 2003). The in situ bio-optical measurements were made during October 1999 at 25 stations (R/V

Bilim cruise). Comparisons of the in situ measurements with the concurrent SeaWiFS retrievals indicate 

that the OC2 and OC4 algorithms are not working satisfactorily in both seas. Case 2 waters dominate the 

Black Sea and the failure of the algorithms is expected. On the other hand, failure of the algorithms in the 

Case 1 waters of the Mediterranean Sea may be due to their specific optical properties. Modifying the OC4 

algorithm to include SeaWiFS information at 412 nm yields a better performance in the Mediterranean Sea 

60 
SIMBIOS Project Annual Report 

without degrading performance in the Black Sea. Combining a local algorithm adapted to oligotrophic 

waters of the Mediterranean Sea and OC4 provides the best results overall. 

300 500 700

0

0.02

0.04

0.06

300 500 700

0

0.01

0.02

0.03

0.04

300 500 700

0

0.005

0.01

0.015

300 500 700

0

0.01

0.02

0.03

1

 NE NM NO EM 

300 500 700

0

0.005

0.01

0.015

0.02

300 500 700

0

0.01

0.02

0.03

0.04

0.05

300 500 700

0

0.1

0.2

0.3

0.4

300 500 700

0

0.02

0.04

0.06

1

 SO SM SE DP 

Figure 5.12: Spectra of the absorption coefficients of the particulate matter (thick line), phytoplankton 

pigment (dash), and detritus (thin line) in different waters in October-November 2001: NE - North 

eutrophic (Chl=0.77 mg⋅m-3); NM- North mesotrophic (Chl=0.17 mg⋅m-3); NO - North oligotrophic 

(Chl=0.11 mg⋅m-3); EM - Equatorial mesotrophic (Chl=0.32 mg⋅m-3); SO - South oligotrophic (Chl=0.07 

mg⋅m-3); SM - South mesotrophic (Chl=0.38 mg⋅m-3); SE - South eutrophic (Chl=4.9 mg⋅m-3); DP - Drake 

Passage (Chl=0.40 mg⋅m-3). 

Atmospheric correction of ocean color via principal component analysis

A methodology has been proposed to retrieve marine reflectance and chlorophyll-a concentration from

space by decomposing the satellite reflectance, Rp, into principal components (Gross-Colzy and Frouin, 

2003). The components sensitive to the ocean signal (Table 5.2, Fig. 5.15) are combined to retrieve the 

principal components of marine reflectance, Rw, allowing reconstruction of marine reflectance and 

estimation of chlorophyll-a concentration. Multi-layered perceptrons are used to approximate the functions 

relating the useful principal components of satellite reflectance to the principal components of marine 

reflectance (Fig. 5.16). The algorithm is developed and evaluated using non-noisy and noisy synthetic data

sets created for a wide range of angular and geophysical conditions. In the absence of noise on satellite 

61 
SIMBIOS Project Annual Report 

October 2001 October 2002 

bbp, m

-1 

Figure 5.13: The mean monthly distributions of values of the particle backscattering coefficient in the Atlantic Ocean 

in October 2001 and 2002 derived from SeaWiFS data. 

October 2001 October 2002 

τa 

Figure 5.14: The mean monthly distributions of values of the aerosol optical thickness over the Atlantic Ocean in 

October 2001 and 2002 derived from SeaWiFS data. 

62 
SIMBIOS Project Annual Report 

400 500 600 700 800 900

−1

−0.5

0

0.5

1

λ (nm)

ep1

400 500 600 700 800 900

−1

−0.5

0

0.5

1

λ (nm)

ep

2 (−), ew1 (−−)

400 500 600 700 800 900

−1

−0.5

0

0.5

1

λ (nm)

ep

3 (−), −ew

2 (−−)

400 500 600 700 800 900

−1

−0.5

0

0.5

1

λ (nm)

ep

4 (−), ew

3 (−−)

400 500 600 700 800 900

−1

−0.5

0

0.5

1

λ (nm)

ep

5 (−), −ew

4 (−−)

400 500 600 700 800 900

−1

−0.5

0

0.5

1

λ (nm)

ep

6 (−), −ew

5 (−−)

400 500 600 700 800 900

−1

−0.5

0

0.5

1

λ (nm)

ep

7 (−), −ew

6 (−−)

400 500 600 700 800 900

−1

−0.5

0

0.5

1

λ (nm)

ep

8

Figure 5.15: Eigenvectors of the Rp and Rw covariance matrices. The epi are displayed for λ ranging from

400 to 900 nm and the ewj for λ ranging from 400 to 700 nm. 

63 
SIMBIOS Project Annual Report 

−1 −0.5 0 0.5

−1

−0.5

0

0.5

desired cw1

computed

(a)

−2.4 −2.35 −2.3 −2.25

−2.4

−2.35

−2.3

−2.25

desired cw2

computed

(b)

3.763.78 3.8 3.823.843.863.88

3.76

3.78

3.8

3.82

3.84

3.86

3.88

desired cw3

computed

(c)

−1.108 −1.104 −1.1

−1.108

−1.106

−1.104

−1.102

−1.1

−1.098

desired cw4

computed

(d)

Figure 5.16. Computed versus desired principal components of marine reflectance, cwj, for j = 1,...,4. 

Figure 5.17: Computed versus desired marine reflectance, Rw, for 6 SeaWiFS wavelengths: (a) 412, 

(b) 443, (c) 490, (d) 510), (e) 555, and (f) 670 nm.

64 
SIMBIOS Project Annual Report 

reflectance, the relative error on marine reflectance does not exceed 2% (Table 5.3, Fig. 5.17). Accurate 

retrieval of the first principal component of marine reflectance allows a global relative error of 5.4% on 

chlorophyll-a concentration (Table 5.3, Figs. 5.18 and 5.19). In the presence of 1% non-correlated and 5% 

spectrally correlated noise on satellite reflectance, the relative error is increased to 6% and 21%, 

respectively. Application to SeaWiFS imagery yields marine reflectance and chlorophyll-a concentration 

fields that resemble those obtained from the standard SeaWiFS processing (Figs. 5.20, 5.21, 5.22 & 5.23), 

but are generally less contrasted. The marine reflectance spectra retrieved by the two algorithms are 

substantially different (Fig. 5.22). A large number of SeaWiFS spectra are characterized in the blue by low 

values not expected in Case-I waters. Accuracy can be improved by including bio-optical variability in the 

simulated marine reflectance ensembles. 

5.4. ACKNOWLEDGMENTS 

We wish to thank the officers, technicians, and scientists that have voluntarily collected SIMBAD data 

and contributed to data analysis, namely Guislain Becu, Jushiro Cepeda-Morales, Martin de La Cruz, JeanMarc Nicolas, and Haili Wang. We also gratefully John McPherson for programming support, and the 

SeaWiFS and SIMBIOS project staff for helping with match-up data and for stimulating discussions. This

work is supported by the National Aeronautics and Space Administration under contract NAS5-00194, the 

Scripps Institution of Oceanography, the California Space Institute, the Centre National d’Etudes Spatiales, 

and the Centre National de la Recherche Scientifique. 

10−1 100 101 2

4

6

8

10

chla (mg m−3)

Rel. er. (%)

(a)

105 115 125 135 145 155 165

2

4

6

8

10

12

scattering angle (o)

chla Rel. er. (%)

(b)

Figure 5.18: Relative error (%) on chlorophyll concentration, chla, as a function of (a) chla and (b) 

scattering angle. 

65 
SIMBIOS Project Annual Report 

10−1 100 101

100

desired chla

computed chla

Without noise

10−1 100 101

100

desired chla

U05C1

10−1 100 101

100

desired chla

computed chla

U1C5

10−1 100 101

100

desired chla

U3C5

Figure 5.19: Computed versus desired chlorophyll concentration, chl a, for various noise figures (see 

text for details).

Figure 5.20: Marine reflectance at 443 nm, Rw(443), derived using neural network (left) and SeaDAS 

(right), for SeaWiFS imagery acquired off South Africa on February 14, 1999. 

66 
SIMBIOS Project Annual Report 

450 500 550 600 650

10−4

10−3

10−2

10−1 NN

λ (nm)

Rw(

λ)

450 500 550 600 650

10−4

10−3

10−2

10−1 SeaDAS

λ (nm)

Rw(

λ)

Figure 5.21: Marine reflectance at 555 nm, Rw(555), derived using neural network (left) and SeaDAS 

(right), for SeaWiFS imagery acquired off South Africa on February 14, 1999. 

 

Figure 5.22: Selected marine reflectance spectra, Rw(λ), obtained using neural network (left) and SeaDAS 

(right), for SeaWiFS imagery acquired off South Africa on February 14, 1999. 

67 
SIMBIOS Project Annual Report 

Figure 5.23. Chlorophyll concentration, chla, in mgm-3 derived from neural network (left) and SeaDAS 

(right), for SeaWiFS imagery acquired off South Africa on February 14, 1999. 

Table 5.2: Correlation coefficients (%) between the eight principal components of Rp, cpi, and the six 

principal components of Rw, cwj. The correlation coefficients between the cpi and τ(550) and chla are also 

indicated. 

cp1 cp2 cp3 cp4 cp5 cp6 cp7 cp8

cw1 12.3 73.3 -39.8 -47.4 16.9 -0.1 -5.7 -8.9

cw2 2.8 17.6 -17.4 56.0 63.6 1.8 29.8 21.2

cw3 3.9 12.0 -2.3 46.8 10.3 -22.9 -45.2 -40.5

cw4 1.2 3.7 -1.6 9.9 -5.3 44.1 -58.1 26.5

cw5 0.0 -1.0 1.2 -3.1 2.0 -38.6 14.7 -14.5

cw6 -0.2 0.1 0.2 -1.3 0.3 -12.9 -4.4 12.0

τ(550) -67.6 22.4 -4.2 15.8 -22.6 22.3 19.9 -38.2

chla 12.1 71.6 -38.0 -51.4 11.0 -0.3 -10.0 -11.3

68 
SIMBIOS Project Annual Report 

Table 5.3: Mean performance of the neural network algorithm in the absence of noise. The tests are 

performed on the whole data ensemble, the RMS error and bias are computed in the physical units of each 

parameter (in mgm-3 for chla). 

RMS Err. Rel. Err. (%) R2

 (%) Bias 

Rw(412) 0.0012 1.7 99.9 0.0000

Rw(443) 0.0007 1.4 99.9 0.0000

Rw(490) 0.0003 0.8 99.9 0.0000

Rw(510) 0.0001 0.5 99.7 0.0000

Rw(555) 0.0002 0.9 99.9 0.0000

Rw(670) 0.0001 1.8 99.9 0.0000

chla 0.841 5.4 99.3 0.0467

REFERENCES 

Li, L. P., H. Fukushima, R. Frouin, B. G. Mitchell, M. X. He, T. Takamura, and S. Ohta, 2003: Influence of 

absorbing aerosols on SeaWiFS-derived marine reflectance during ACE-Asia. In Ocean Remote

Sensing and Applications, R. Frouin, Y. Yuan, and H. Kawamura, Editors, Proceedings of SPIE Vol. 

4892, pp. 105-115. 

Li, L. H. Fukushima, R. Frouin, B. G. Mitchell, M-X He, I. Uno, T. Takamura, and S. Ohta, 2003:

Influence of sub-micron absorptive aerosol on SeaWiFS-derived marine reflectance during ACE-Asia,

J. Geophys. Res.,in press.

Frouin, R., O. Kopelevich, V. Burenkov, A. Demidov, A. Grigoriev, A. Khrapko, S. Sheberstov, and S. 

Vazyulya, 2003: Variability of the bio-optical characteristics on the Atlantic transect between 50o

N 

and 55oS in two different seasons from satellite and ship data. In Current Problems in Optics of natural

Waters, K. Shifrin, I. Levin and G. Gilbert, Editors, Proceedings of ONW-2003, Saint-Petersburg, 

Russia, in press. 

Gross-Colzy, L., R. Frouin, C. M. Pietras, and G. S. Fargion, 2003: Non-supervised classification of 

aerosol mixtures for ocean color remote sensing. Ocean Remote Sensing and Applications, R. Frouin, 

Y. Yuan, and H. Kawamura, Editors, Proceedings of SPIE Vol. 4892, pp. 95-104. 

Gross-Colzy, L., and R. Frouin, 2003: Self-organized mapping of aerosol mixtures at AERONET coastal

and island sites. IEEE Neural Network for Signal Processing Workshop 2003, Toulouse, France, in 

press. 

Sancak, S., S. T. Besiktepe, a. Yilmaz, M. Lee, B. G. Mitchell, and R. Frouin, 2003: Evaluation of 

SeaWiFS chlorophyll-a in the Black and Mediterranean Seas. Int. J. Remote Sensing, accepted. 

69 
SIMBIOS Project Annual Report 

Shettle E.P. and R.W. & Fenn, 1979: Models for the aerosol of the lower atmosphere and the effects of 

humidity variations on their optical properties. AFGL Tech. Rep., 105, D8, 9-887-9901. 

This Research was Supported by 

the NASA Contract # 00194

Publications 

Deschamps, P.-Y., B. Fougnie, R. Frouin, P. Lecomte, and C. Verwaerde, 2003: SIMBAD: A field 

radiometer for satellite ocean-color validation. Appl. Opt., submitted. 

Dubuisson, P., D. Dessailly, M. Vesperini, and R. Frouin, 2003: Water vapor retrieval over ocean using 

near-infrared radiometry. J. Geophys. Res., submitted.

Frouin, R., and S. F. Iacobellis, 2002: Influence of phytoplankton on the global radiation budget. J. 

Geophys. Res., 107, 4377, doi: 10.1029/2001JD000562. 

Frouin, R., and G. D. Gilbert, Editors, 2002: Remote Sensing and Underwater Imaging. Proceedings of 

SPIE Vol. 4488, 280 pp. Published by SPIE--The International Society for Optical Engineering, 

Bellingham, Washington, USA. [Book]

Frouin, R., G. D. Gilbert, and D. Pan, Editors, 2003: Ocean Remote Sensing and Imaging II. Proceedings of 

SPIE Vol. 5155, in press. Published by SPIE –the International Society for Optical Engineering, 

Bellingham, Washington, USA.[Book]

Frouin, R., Y. Yuan, and H. Kawamura, Editors, 2003: Ocean Remote Sensing and Applications. 

Proceedings of SPIE Vol. 4892, 620 pp. Published by SPIE--The International Society for Optical

Engineering, Bellingham, Washington, USA. [Book]

Frouin, R., L. Gross-Colzy, and P.-Y. Deschamps, 2003: Ocean color remote sensing without explicit 

atmospheric correction. In Ocean Remote Sensing and Applications, R. Frouin, Y. Yuan, and H. 

Kawamura, Editors, Proceedings of SPIE Vol. 4892, pp. 133-142. 

Frouin, R., B. A. Franz, and P. J. Werdell, 2003: The SeaWiFS PAR product. In Algorithm Updates for the 

Fourth SeaWiFS Data Reprocessing, S. B. Hooker and E. R. Firestone, Editors, NASA/TM-2003-

206892, Vol. 22, pp. 46-50.

Frouin, R., O. Kopelevich, V. Burenkov, A. Demidov, A. Grigoriev, A. Khrapko, S. Sheberstov, and S. 

Vazyulya, 2003: Variability of the bio-optical characteristics on the Atlantic transect between 50oN 

and 55oS in two different seasons from satellite and ship data. In Current Problems in Optics of natural

Waters, K. Shifrin, I. Levin and G. Gilbert, Editors, Proceedings of ONW-2003, Saint-Petersburg, 

Russia, in press. 

Fukushima, H., M. Toratani, A. Tanaka, W. Chen, H. Murakami, R. Frouin, and B. G. Mitchell, 2003:

ADEOS-II/GLI ocean color atmospheric correction: Early phase result. In Ocean Remote Sensing and 

Imaging II, R. Frouin, G. D. Gilbert, and D. Pan, Editors, Proceedings of SPIE Vol. 5155, in press. 

Gross-Colzy, L., and R. Frouin, 2003a: Self-organized mapping of aerosol mixtures at AERONET coastal

and island sites. IEEE Neural Network for Signal Processing Workshop 2003, Toulouse, France, in 

press. 

Gross-Colzy, L., R. Frouin, C. M. Pietras, and G. S. Fargion, 2003: Non-supervised classification of 

aerosol mixtures for ocean color remote sensing. In Ocean Remote Sensing and Applications, R. 

Frouin, Y. Yuan, and H. Kawamura, Editors, Proceedings of SPIE Vol. 4892, pp. 95-104. 

70 
SIMBIOS Project Annual Report 

Gross-Colzy, L., and R. Frouin, 2003b: Remote sensing of chlorophyll concentration from space by 

principal component analysis of atmospheric effects. In Ocean Remote Sensing and Imaging II, R. 

Frouin, G. D. Gilbert, and D. Pan, Editors, Proceedings of SPIE Vol. 5155, in press. 

Gross-Colzy, L., C. Dupouy, R. Frouin, J.-M. André, and S. Thiria, 2003: Reducing variability due to 

secondary pigments in the retrieval of chlorophyll concentration from marine reflectance: A case study

in the western equatorial Pacific. Appl. Opt., submitted.

Knapp, K., R., and R. Frouin, 2003: Separating the aerosol signal from surface effects in geostationary 

satellite measurements. J. Geophys. Res., submitted. 

Li, L. H. Fukushima, R. Frouin, B. G. Mitchell, M-X He, I. Uno, T. Takamura, and S. Ohta, 2003:

Influence of sub-micron absorptive aerosol on SeaWiFS-derived marine reflectance during ACE-Asia,

J. Geophys. Res., in press.

Li, L. P., H. Fukushima, R. Frouin, B. G. Mitchell, M. X. He, T. Takamura, and S. Ohta, 2003: Influence of 

absorbing aerosols on SeaWiFS-derived marine reflectance during ACE-Asia. In Ocean Remote

Sensing and Applications, R. Frouin, Y. Yuan, and H. Kawamura, Editors, Proceedings of SPIE Vol. 

4892, pp. 105-115. 

Loisel, H., J.-M. Nicolas, P.-Y. Deschamps, and R. Frouin, 2002: Seasonal and inter-annual variability of 

particulate organic matter in the global ocean. Geophys. Res. Lett., 29, doi:10.1029/2002/GL015948.

Meister, G., P. Abel, R. Barnes, J. Cooper, C. Davis, G. Fargion, R. Frouin, M. Godin, D. Korwan, R.

Maffione, C. McClain, S. McLean, D. Menzies, A. Poteau, J. Robertson, and J. Sherman, 2003:

Comparison of spectral radiance calibrations at oceanographic and atmospheric research laboratories.

Metrologia, 40, pp. S93-S96. 

Miller, A. J., M. A. Alexander, G. J. Boer, F. Chai, K. Denman, D. J. Erikson, R. Frouin, A. Gabric, E. 

Laws, M. Lewis, Z. Liu, R. Murtugudde, S. Nakamoto, D. J. Nielson, J. R. Morris, C. Ohlman, I. 

Perry, N. Schneider, K. Shell, and A. Timmermann, 2003: Potential feedbacks between Pacific Ocean 

ecosystems and inter-decadal climate variations. Bull. Am. Meteor. Soc., 84, pp. 617-633. 

Nakamoto, S. S. Prasanna-Kumar, J. M. Oberhuber, H. Saito, K. Muneyama, and R. Frouin, 2002:

Chlorophyll modulation of mixed layer thermodynamics in a mixed layer-sopycnal general circulation 

model--An example from the Arabian Sea and the Equatorial Pacific. Proc. Indian Acad. Sci. (Earth

Planet. Sci.), 111, pp. 339-349. 

Nakamoto, S., K. Muneyama, T. Sato, S. Prasanna Kumar, A. Kumar, and R. Frouin, 2002: Ocean biogeophysical modeling using mixed layer-isopycnal general circulation model coupled with

photosynthesis process. Recent Research Development in Geophysics, 4, pp. 9-20, Researchsigpost,

Trivandrum-695 023, Kerala, India.

Nakamoto, S. S. Prasanna-Kumar, K. Nakata, K. Shell, R. Frouin, K. Ueyoshi, P. Sammarco, A. Lai, H.

Saito, T. Sato, and K. Muneyama, 2003: Thermophysical Interaction of Ocean Ecosystem and 

Geophysical system--For Understanding Carbon Circulation in the Ocean. J. Adv. Mar. Sci. Tech. 

Soc., 8, pp. 35-48. 

Sancak, S., S. T. Besiktepe, a. Yilmaz, M. Lee, B. G. Mitchell, and R. Frouin, 2003: Evaluation of 

SeaWiFS chlorophyll-a in the Black and Mediterranean Seas. Int. J. Remote Sensing, accepted. 

Shell, K., R. Frouin, S. Nakamoto, and R. C. J. Somerville, 2003: Atmospheric response to solar radiation 

absorbed by phytoplankton, J. Geophys. Res., 108, doi: 10.1029/2003JD003440. 

71 
SIMBIOS Project Annual Report 

Smirnov, A., B. N. Holben, O. Dubovik, R. Frouin, and I. Slutsker, 2003: Maritime component in aerosol 

optical models derived from AERONET (Aerosol Robotic Network) data, J. Geophys. Res., 108, 4033, 

doi: 10.1029/2002JD002701. 

Souaidia, N., C. Pietras, S. Brown, K. Lykke, P.-Y. Deschamps, G. Fargion, and B. C. Johnson, 2003: Sun 

photometer laser- and lamp-based radiometric calibrations: Comparison with the Langley technique 

and implications on remote sensing. In Ocean Remote Sensing and Imaging II, R. Frouin, G. D. 

Gilbert, and D. Pan, Editors, Proceedings of SPIE Vol. 5155, in press. 

Zhao, X.-P., I. Laszlo, O. Dubovik, A. Smirnov, B. H. Holben, J. Sapper, C. Pietras, K. J. Voss, and R.

Frouin, 2003: Regional evaluation of the revised NOAA/NESDIS AVHRR two-channel aerosol

retrieval algorithm and determination of the refractive index for key aerosol types over the ocean. J. 

Geophys. Res., in press. 

72 
SIMBIOS Project Annual Report 

Chapter 6 

Merging Ocean Color Data From Multiple Missions 

Watson W. Gregg 

NASA Goddard Space Flight Center, Greenbelt, Maryland 

6.1 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, NASA and the international community have established high priority satellite missions

designed to acquire and produce high quality ocean color data (Table 6.1). Ten 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), MODIS-PM, Super-GLI (S-GLI), and the Visible/Infrared 

Imager and Radiometer Suite (VIIRS) on the NPOESS Preparatory Project (NPP) and the National Polarorbiting Operational Environmental Satellite System (NPOESS). 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 chlorophyll concentrations and other related parameters. 

Many contain additional bands that can provide important ancillary information about the optical and biological 

state of the oceans. 

In previous efforts, we have established that better ocean coverage can be obtained in less time if the data 

from several missions are combined (Gregg et al., 1998; Gregg and Woodward, 1998). In addition to improved 

coverage, data can be taken from different local times of day if the missions are placed in different orbits, which 

they are. This can potentially lead to information on diel variability of phytoplankton abundances. Since 

phytoplankton populations can increase their biomasses by more than double in a single day under favorable

circumstances (Eppley, 1972; Doney at el., 1995), observations of their abundances at different times within a 

single day would be useful.

We proposed to investigate, develop, and test algorithms for merging ocean color data from multiple 

missions. We seek general algorithms that are applicable to any retrieved Level-3 (derived geophysical

products mapped to an Earth grid) 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 emphasize 3 primary methods: 1) averaging, 2) blending, and 3) statistical interpolation. 

6.2. RESEARCH ACTIVITIES 

We investigated a set of 3 merging algorithms utilizing Level-3 data products. None of the candidate 

algorithms were limited to any Level-3 grid size or temporal frequency. The choice of grid size and frequency

issue depends on how sparse the final fields are and the acceptance level for data gaps. We leave this choice to 

the SIMBIOS Project. For our analyses, however, we used 25-km equiangular spatial, and daily time fields. 

Candidate merger algorithms under investigation in this proposed effort were: averaging, blending, and 

statistical (optimal) interpolation.

73 
SIMBIOS Project Annual Report 

Averaging

This method is a simple, straightforward application of weighting data from each sensor equally. At 

grid points where only data from one satellite are available, it enters the merged field unadjusted. 

s ijs

ij

s ijs

C

C

n

∑

∑

where C indicates chlorophyll from sensor s, n is the number of observations from sensor s,ij represents the 

Level-3 grid point in question, and the summations are over the sensors. Although we use chlorophyll to

represent the equation, any Level-3 data product can be used. This method has the advantage of simplicity

and total objectivity, i.e., no sensor data are preferred over others. It can potentially suffer from this same

objectivity in the case of relatively poorer performance. If Level-3 grid locations are common among the 

different sensor products, the application of the method is straightforward. If they are not, then interpolation 

may be required. 

Blended analysis 

The blended analysis has traditionally been applied to merging satellite and in situ data (Reynolds, 

1988). Also known as the Conditional Relaxation Analysis Method (CRAM; Oort, 1983), this analysis 

assumes that in situ data are valid and uses these data directly in the final product. The satellite chlorophyll 

data are inserted into the final field using Poisson's equation 

2 b ∇ C =ψ

where Cb

 is the final blended field of chlorophyll, and Ψ is a forcing term, which is defined to be the 

Laplacian of the gridded satellite chlorophyll data (∇2

S). In situ data serve as internal boundary conditions, 

and are inserted directly into the solution field Cb 

C I ibc =

where the subscript ibc indicates internal boundary condition (IBC) and I is the in situ value of chlorophyll. 

Thus in situ data appear un-adjusted in the final blended product. In its application to multiple ocean color 

data sets, in situ data would be replaced by a determination of superior performance by one of the sensors 

data, and utilized as the IBC. This could occur across the domain for an individual sensor, if its calibration

was considered superior, for example. Or it could occur by location as the environmental conditions 

provide for better performance of one sensor over the others (e.g., location of sun glint, individual scan 

problems, etc.). Where one sensor data could be established as superior, it would serve as the IBC. If no 

distinction could be provided, the data could simply be merged using one or more of the other methods. 

Then the final merged product would be blended, so that the internal boundary conditions are upheld, and 

the rest of the Level-3 field is adjusted according to the spatial variability of the other sensors. This can 

provide a bias correction to the non-IBC points, while retaining their spatial structure, and potentially

produce an overall enhanced data set.

The requirement of superior data field insertion unaltered into the merged field can be relaxed. For 

example, the IBC weight could be 0.25 for sensor 1 and 0.75 for sensor 2 at grid point ij. This can be a 

useful modification of several sensor data sets are superior to others but not necessarily from one another, 

or if clear superiority is difficult to quantify. 

Statistical Interpolation 

This method is often referred to as optimal interpolation (e.g., Reynolds and Smith, 1994), but is

technically only optimal when all of the error correlations are known (Daley, 1991), which is rare. In this

method the weights W are chosen to minimize the expected error variance of the analyzed field (Daley, 

74 
SIMBIOS Project Annual Report 

1991). It differs from the spatial analysis method by allowing error correlations to determine the effective 

separation distance, and from the blended analysis by use of a statistical approach for defining the weights. 

A weight matrix W represents the error correlations, and is referred to as the error covariance matrix. 

1, , ( ) C C ij = + sij ∑Wijkm CS + km −Cs km

This method has the advantage of widespread use in data assimilation problems, and objectivity in selection 

of the weights. The disadvantage is the statistical interpretation of the merged data set, as opposed to a 

scientific evaluation.

It is possible that the best merging method will be one that utilizes combinations of these algorithms. 

For example, some level of subjective analysis will be used in data masking, and then averaged. Reynolds

and Smith (1994) combine the use of blending for bias correction followed by statistical interpolation to

recover the grid resolution. We may easily envision a combination of approaches. 

Table 6.1. Mission characteristics of proposed and present global ocean color sensors. For node, D indicates 

descending, A indicates ascending. Incl. Indicate inclination (degrees). ECT means local equator crossing time

on the node. GIFOV means ground instantaneous field of view at nadir. Advanced Earth Observing Satellite 

(ADEOS); Earth Observing System (EOS); Environmental Satellite (Envisat); NPOESS Preparatory Project 

(NPP) and National Polar-orbiting Operational Environmental Satellite System (NPOESS). 

Sensor Launch Spacecraft Altitude Incl. ECT Node Swath Tilt GIFOV 

SeaWiFS 1997 OrbView-2 705 km 98.2 noon D 45 o ±20 o 1 km

MODIS-AM 1999 EOS-Terra 705 km 98.2 10:30 AM D 55 o none 1 km

MERIS 2002 Envisat 780 km 98.5 10:00 AM D 41 o none 1 km

GLI 2003 ADEOS-II 803 km 98.6 10:30 AM D 45 o ±20 o 1km

POLDER-II 2003 ADEOS-II 803 km 98.6 10:30 AM D 51 o ±20 o 7 km

MODIS-PM 2002 EOS-Aqua 705 km 98.2 1:30 PM A 55o

 none 1 km

S-GLI 2005 ADEOS-III 803 km 98.6 10:30 AM D 45 o ±20 o 1 

VIIRS 2006 NPP TBD TBD TBD TBD TBD TBD 1 km

VIIRS 2009 NPP TBD TBD TBD TBD TBD TBD 1 km

6.3. RESEARCH RESULTS

The three candidate merger algorithms have been tested using SeaWiFS and MODIS data. The 

SeaWiFS data is Version 4 and the MODIS is Collection 4. Results indicate promising behavior from all

three candidate algorithms (Figure 6.1). However, there are some problems remaining, most associated 

with data quality of the sensors and our ability to understand and correct for them prior to application of the 

algorithms. Overall the averaging method is best for data with no biases, because it is simple, objective, 

and computationally fast. If there are biases in either or both data sets that are uncorrected or 

unrecognized, this method will propagate these errors into the merged field, and produce a poor quality 

data set. Knowledge of biases in the new versions of each sensor is presently lacking, and requires 

substantial effort. The blended method is effective at eliminating biases if a "truth field" can be identified. 

In the analyses done so far, we assumed SeaWiFS to be a truth field unilaterally, and MODIS was the data

blended to produce the final merged product. The effectiveness of the bias-correction capability of the 

blended analysis is quite well known in in situ-satellite data merging, but not in satellite-satellite merging. 

Our results indicate that significant differences in satellite data quality coupled with the very large coverage 

of both sensors, results in over-correction by the blended method. 

The statistical (optimal) interpolation (OI) method has many of the advantages of the blended method 

in bias-correction. However, the over-correction behavior of the blended method is reduced to the point

that it is not readily apparent in the resulting merged field. The method suffers from computational

complexity and is very slow. 

75 
SIMBIOS Project Annual Report 

 

Figure 6.1: Comparison of 3 different merging methodologies for SeaWiFS and MODIS on Dec. 2, 2000 

It is clear that proper selection of merging algorithms depends critically upon knowledge of the error 

characteristics of the data sets being merged. Consequently, we invested significant effort into analyzing the 

SeaWiFS and MODIS data behavior as compared against the SeaBASS data archive. 

SeaWiFS Reprocessing 3 vs. Reprocessing 4 

Comparisons were made between the newly reprocessed SeaWiFS Level-3 chlorophyll product

(Reprocessing 4 or R4) and the previous version (Reprocessing 3 or R3) using in situ measurements. There 

were 2,470 SeaWiFS/SeaBASS match-up measurements of fluorometrically/spectrophotometricallyderived chlorophyll-a (mg m-3) at depths of 0.0 to 10.0 meters available for the SeaWiFS mission period of 

September 15, 1997 through June 1, 2002 (Figure 6.2). The results showed that the newly reprocessed 

SeaWiFS data matched up better with the surface measurements than the previous version did (Figure 6.3). 

Globally, the slope of the match-ups improves to 0.85 from 0.78 in log-log scale. A significant trend that

76 
SIMBIOS Project Annual Report 

contributed to this improvement was the overall decrease in SeaWiFS chlorophyll levels less than 1.0 mg

m-3. Regional analyses reveal that the match-ups improve in every oceanic basin, except the Antarctic. 

However, SeaWiFS continues to exhibit poor correspondence with in situ data in the North Atlantic where 

the match-ups have a slope of 0.54. 

Figure 6.2: Global distribution of the SeaBASS measurements with a co-located SeaWiFS pixel for both 

R3 and R4 (N=2,470). The oceanic regions used for statistical comparisons are shown. 

An examination of monthly images for May 1999 revealed that the number and magnitude of highvalue chlorophyll pixels had increased in the high latitude open ocean of the South Pacific. There were 

more high-value outliers in the R4 image as compared to the R3 in the open ocean of the South Pacific, an 

area generally characterized by relatively homogeneous levels of low-chlorophyll. On a monthly timescale 

and in a largely homogenous area, increased numbers and magnitudes of high outliers in the new R4 data 

set as compared to the previous version is some cause for concern. The incidence of outliers should tend to 

be relatively low in monthly images in general due to the smoothing effects of averaging the dailies. In

addition, the lower latitude areas such as the South Pacific may have fewer valid daily measurements than 

in other areas, but an examination of the daily images for this region exhibited the same general pattern 

shown in the monthly values. These results were published as a NASA Technical Memorandum (Casey

and Gregg, 2003). 

Global and Regional SeaWiFS Chlorophyll Data Evaluation 

The SeaWiFS chlorophyll data set was compared to comprehensive archives of in situ chlorophyll data

from NASA and NOAA, involving 4168 point-to-point daily matchups. The global comparison indicated 

77 
SIMBIOS Project Annual Report 

an RMS log error of 31%, with a coefficient of determination (r2) of 0.76 (Figure 6.5). RMS log error for 

open ocean (defined as bottom depth > 200m) was 5% lower than for coastal regions, indicating a small 

deterioration of quality of the SeaWiFS data set in coastal regions. All of the Pacific oceanographic basins 

generally showed very good agreement with SeaWiFS, as did the South Atlantic basin. However, poorer 

agreement was found in the Mediterranean/Black Seas, North Central and Equatorial Atlantic basins, and 

the Antarctic. Optical complexity arising from riverine inputs, Saharan dust, and anomalous oceanic 

constituents contributed to the differences observed in the Atlantic, where a trend of overestimation by

SeaWiFS occurred. The Antarctic indicated a pronounced negative bias, indicating an underestimation, 

especially for chlorophyll concentrations > 0.15 mg m-3. The results provide a comprehensive global and 

geographic analysis of the SeaWiFS data set, which will assist data users and policy makers in assessing

the uncertainty of estimates of global and regional ocean chlorophyll and primary production. The results 

have been submitted to Remote Sensing of Environment (Gregg and Casey 2003). 

Figure 6.3: Scatterplots comparing SeaBASS chlorophyll measurements (mg m-3 ) with co-located 

SeaWiFS values for versions R3 (top) and R4 (bottom). The 1-to-1 line (thick) and the least-squares 

regression line (thin) are shown, as well as regression coefficients, root mean squared differences 

(RMSDs), and biases. 

78 
SIMBIOS Project Annual Report 

Figure 6. 4: (Top) Global map showing the location of the South Pacific region between -131.6 and -87.8 

degrees longitude and -59.6 and -38.6 degrees latitude (500 x 240 pixels). (Bottom) Contour plot of the 

monthly SeaWiFS R4 chlorophyll values (mg m-3) which have a co-located R3 value for the South Pacific 

region in May 1999. 

79 
SIMBIOS Project Annual Report 

RMS

0.0

10.0

20.0

30.0

40.0

50.0

60.0

RMS % log Error

RMS % 38.5 27.5 12.7 20.1 24.0 16.8 48.5 24.7 35.8 55.7 31.6 33.1 31.0

Antarct S Indian S Pacific S 

Atlantic

Eq 

Indian

Eq 

Pacific

Eq 

Atlantic

N Cen 

Pacific

N Cen 

Atlantic

Med/

Black 

Sea

N 

Pacific

N 

Atlantic Global

Bias 

-30.0

-20.0

-10.0

0.0

10.0

20.0

30.0

40.0

50.0

Bias % log

Bias % -18.1 -7.0 -3.0 10.0 12.4 0.5 29.4 0.9 14.3 44.7 4.1 7.9 6.2

Antarct S Indian S Pacific S 

Atlantic

Eq

Indian

Eq

Pacific

Eq

Atlantic

N Cen 

Pacific

N Cen

Atlantic

Med/

Black 

Sea

N Pacific N 

Atlantic Global

Figure 6.5: Top: RMS error between in situ data and the SeaWiFS chlorophyll data set, separated into the 13 

major oceanographic basins, and global. Bottom: average error or bias. Dashed lines indicate the global mean. 

Global and Regional MODIS Chlorophyll Data Evaluation 

The same methodology for evaluation of SeaWiFS was used to evaluate MODIS chlorophyll data. Only

validated data from Collection 4 were used, spanning the period Nov. 2000 through Mar. 2002. In situ data

were exclusively from the SeaBASS archive, as no NODC data were available. 

 There are 3 different bio-optical algorithms for chlorophyll from MODIS. Two (Chlor-MODIS and Chlor-a2) are empirical expressions, while the third (Chlor-a-3) is a semi-analytic algorithm. Chlor-a-2 is the 

algorithm most similar to the SeaWiFS bio-optical algorithm.

80 
SIMBIOS Project Annual Report 

All three bio-optical algorithms from MODIS produced log RMS error compared to in situ data

comparable with SeaWiFS over the same time period (Figure 6.6). There were minor differences among the 

evaluations regionally as well. The North Atlantic basin was an exception, where the MODIS Chlor-MODIS 

and Chlor-a-2 algorithms performed much worse than SeaWiFS and the MODIS Chlor-a-3 algorithm. Overall 

these results suggest compatibility of the two chlorophyll data sets and supports initial merging analyses. 

MODIS/SeaWiFS Chlorophyll Comparison vs. In Situ Data (11/2000-3/2002)

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

40.0

45.0

RMS %

chl_modis

chlor_a_2

chlor_a_3

SeaWiFS

chl_modis 26.6 7.8 24.6 18.2 13.8 33.2 38.9 35.9 32.5

chlor_a_2 26.6 9.9 21.8 17.9 14.7 30.4 32.7 27.8 28.0

chlor_a_3 27.7 16.7 25.5 17.0 19.2 26.8 38.9 19.1 29.1

SeaWiFS 27.7 12.7 22.4 15.0 19.7 28.7 34.7 17.1 27.0

Antarctic 

(n=40)

S Pacific 

(n=3)

S Atlantic

(n=25)

Eq Pacific

(n=100)

Eq Atlantic

(n=10)

N Central

Pacific 

(n=175)

N Central

Atlantic

(n=175)

N Atlantic

(n=60)

Global

(n=600)

Gregg and

Casey, GSFC

04/03/03

Figure 6.6: Comparison of MODIS chlorophyll algorithms with SaeBASS in situ data globally and by region. 

SeaWiFS chlorophyll comparisons for the same time period are also shown. 

Analyses of Decadal Changes in Ocean Primary Production 

Although tangential to the main focus of our proposed effort, the SIMBIOS grant supported our work on 

analysis of decadal changes in global ocean primary production by providing in situ chlorophyll from the 

SeaBASS data set for blending and reduction of residual errors in the SeaWiFS data set. In this analysis, we 

found that satellite-in situ blended ocean chlorophyll records indicate that global ocean annual primary 

production has declined more than 6% since the early 1980’s (Figure 6.7). Nearly 70% of the global

decadal decline occurred in the high latitudes. In the northern high latitudes, these reductions in primary 

production corresponded with increases in sea surface temperature and decreases in atmospheric iron 

deposition to the oceans. In the Antarctic, the reductions were accompanied by increased wind stress. 

Three of four low latitude basins exhibited decadal increases in annual primary production. These results 

indicate that ocean photosynthetic uptake of carbon may be changing as a result of climatic changes and 

suggest major implications for the global carbon cycle. These results were published in Geophysical

Research Letters (Gregg et al., 2003). 

6.4 RECOMMENDATIONS 

Comparisons between MODIS and in situ data from the SeaBASS archives for the period Nov. 2000 

through Mar. 2002 indicated no significant difference from similar comparisons of SeaWiFS for the same time 

81 
SIMBIOS Project Annual Report 

period. This period is referred to as the validated MODIS time period. We therefore recommend the averaging 

methodology for merging MODIS and SeaWiFS chlorophyll. For time periods following the validated period, 

we have no knowledge of biases between the data sets, and also recommend the averaging methodology, which 

is the correct choice for these conditions. 

For time periods preceding the MODIS validated period, biases are known to be present, and they vary in 

time and space. This averaging is not the proper choice for merging. However, the biases have not been fully 

characterized. Nevertheless, application of the blended analysis and statistical interpolation are appropriate for 

this time period. They both suffer from drawbacks, however, that limits their effective application. The 

blended analysis is subject to over-correction and produces artificially elevated chlorophyll values in places. 

The statistical interpolation, produces a more reasonable final merged analysis field, but is so computationally

expensive as to limit its use. Further analysis is required to modify both methods prior to their implementation 

for satellite data merging, but both show promising results in the case where biases are known. In these 

circumstances one or both of these methods are superior to averaging because of the correction aspect of the 

methods. Averaging in the case of known biases results in degradation of the merged data set and is not

recommended. If biases in the satellite data sets cannot be removed in later reprocessing, then additional effort 

should be expended on blending and statistical interpolation to correct for these errors in data merging. 

The SeaBASS chlorophyll data set has been an invaluable resource for evaluating data merging 

methodologies and it or a similar data set are required for further efforts. It has also been essential in analysis

of long-term trends of global satellite chlorophyll as indicated by the identification of a 6% decline in global 

primary production, a finding that would not have been possible without the use of SeaBASS chlorophyll data

for blending with SeaWiFS to reduce residual errors in the satellite data set. It is essential to continue this 

effort, or something similar, to improve data merger methodologies and to further our understanding of satellite

chlorophyll trends. 

Change (SeaWiFS-CZCS) SST

S Indian

S Pac S Atl

N Ind

Eq Ind

Eq Pac

Eq Atlantic

NC Pac

NC Atl

N Pacific

N Atl

Antarc

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Degrees C

Southern Basins Equatorial Basins Northern Basins

* * * * * * * * *

Change (SeaWiFS-CZCS) Iron Deposition

AntarcS Indian

S Pacific

S Atl

Eq Indian

Eq Pac

Eq Atlantic

N Cen Pacific

NC Atl N Atl

N Indian

N

 Pacific

-80

-60

-40

-20

0

20

40

60

80

Percent Change

Southern Basins Equatorial Basins Northern Basins

* * * * * * * * * * * *

Change (SeaWiFS-CZCS) Scalar Wind Stress

S Indian

S Pacific

S Atl

Eq Ind

Eq Atl

NC Pac

NC Atl N Pac N Atl

Eq 

Pacific

N 

Indian

Ant

arctic

-10

-5

0

5

10

15

20

Percent Change

Southern Basins Equatorial Basins Northern Basins

* * *

Change (SeaWiFS-CZCS) Annual PrimaryProduction

Eq Pacific

-1.6

-1.4

-1.2

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

Pg C y-1

Southern Basins Equatorial Basins Northern Basins

* * * * * * * * * * *

Antarctic

 S

Indian

 S

Pacific

 S

Atlantic

 N

Indian

 Eq

Indian

 Eq

Atlantic

N Cen

Pacific

N Cen

Atlantic

 N

Pacific

 N

Atlan

Figure 6.7: Differences between SeaWiFS (1997-2002) and CZCS (1979-1986) in the 12 major oceanographic 

basins. Differences are expressed as SeaWiFS-CZCS. Top left: Annual primary production (Pg C y-1). An 

asterisk indicates the difference is statistically significant at P < 0.05. Top right: SST (degrees C). Bottom left: 

iron deposition (%). Bottom right: mean scalar wind stress (%). 

82 
SIMBIOS Project Annual Report 

REFERENCES 

Casey, N.W. and W.W. Gregg, 2003: Comparing Seawifs Reprocessing Versions (R3 vs. R4). NASA

Technical Memorandum 2003-212235. 20 pp. 

Daley, R., 1991: Atmospheric Data Analysis, Cambridge Univ. Press, Cambridge. 457 pp. 

Doney, S.C., R.M. Najjar, and S.E. Stewart, 1995: Photochemistry, Mixing, and Diurnal Cycles in the Upper 

Ocean. J. Mar. Res. 53, 341-369. 

Eppley, R.W., 1972. Temperature And Phytoplankton Growth In The Sea. Fish. Bull., 70, 1063-1085. 

Gregg, W.W. and N.W. Casey, 2003: Global And Regional Evaluation Of The Seawifs Chlorophyll Data

Set. Remote Sensing of Environment, submitted.

Gregg, W.W., M.E. Conkright, P. Ginoux, J.E. O’Reilly, and N.W. Casey, 2003: Ocean Primary 

Production And Climate: Global Decadal Changes. Geophysical Research Letters, 30 (15) 1809, doi:

10.1029/2003GL016889. 

Oort, A.H., 1983: Global Atmospheric Circulation Statistics, 1958-1973, NOAA Prof. Paper 14, 

 180 pp. 

Reynolds, R.W., 1988: A Real-Time Global Sea Surface Temperature Analysis. J. Clim., 1,75-86. 

Reynolds, R.W. and T.M. Smith, 1994: Improved Global Sea Surface Temperature Analyses Using 

Optimum Interpolation. J. Clim., 7, 75-86. 

83 
SIMBIOS Project Annual Report 

Chapter 7 

Bio-Optical and Remote Sensing Observations in 

Chesapeake Bay 

Lawrence W. Harding Jr. and Andrea Magnuson 

University of Maryland Center for Environmental Science, Cambridge, Maryland 

7.1 INTRODUCTION 

The high temporal and spatial resolution of satellite ocean color observations will prove invaluable for 

monitoring the health of coastal ecosystems where physical and biological variability demands sampling

scales beyond that possible by ship. However, ocean color remote sensing of Case 2 waters is a challenging 

undertaking due to the optical complexity of the water. The focus of this SIMBIOS support has been to

provide in situ optical measurements from Chesapeake Bay (CB) and adjacent mid-Atlantic bight (MAB) 

waters for use in algorithm development and validation efforts to improve the satellite retrieval of

chlorophyll (chl a) in Case 2 waters. CB provides a valuable site for validation of data from ocean color 

sensors for a number of reasons. First, the physical dimensions of the Bay (> 6,500 km2

) make retrievals 

from satellites with a spatial resolution of ~1 km (i.e., SeaWiFS) or less (i.e., MODIS) reasonable for most 

of the ecosystem. Second, CB is highly influenced by freshwater flow from major rivers, making it a 

classic Case 2 water body with significant concentrations of chlorophyll, particulates and chromophoric

dissolved organic matter (CDOM) that highly impact the shape of reflectance spectra. Finally, past and 

ongoing research efforts provided an extensive data set of optical observations that support the goal of this

project. Our SIMBIOS contribution has the following objectives:

• To provide to SeaBASS an on-going stream of in-situ optical measurements from CB/MAB for 

use by the ocean color community in the development and validation of atmospheric correction 

and ocean bio-optical models; 

• To evaluate the performance of SeaWiFS bio-optical algorithms in CB/MAB;

• To evaluate the performance of SeaWiFS LWN and chl a products in CB/MAB on a wide range of 

time and space scales; 

• To utilize our bio-optical database to develop a regionally-tuned algorithm for CB/MAB; 

• To produce accurate satellite chl a fields that can be used in existing primary productivity models 

(Harding et al., 2002) to improve the spatial and temporal resolution of primary productivity

estimates for CB/MAB. 

7.2 RESEARCH ACTIVITIES 

Field Program 

Throughout our SIMBIOS support we have maintained a seasonal (i.e., spring, summer, fall) sampling 

schedule in CB proper that started in 1995 with the NSF Land-Margin Ecosystem Research (LMER)

program Trophic Interactions in Estuarine Systems (TIES) cruises (1995-2000) and continued with the NSF 

Biocomplexity cruises (2001-2003). In addition, five separately funded Office of Naval Research (ONR) 

cruises were conducted in adjacent offshore waters including coverage to the Gulf Stream. All cruises 

collected the same suite of measurements. Vertical profiles of downwelling irradiance (Ed) and upwelling 

radiance (Lu) were preformed with a Biospherical Instruments MER 2040/2041. Discrete samples of 

particulate and dissolved fractions were collected for determination of absorption spectra of phytoplankton, 

aph(λ), non-pigmented particulates, ad(λ), and colored dissolved material, aCDOM(λ). Discrete particulate 

84 
SIMBIOS Project Annual Report 

samples were collected for pigment analyses of chl a by fluorometry and a suite of pigments by HPLC. 

Radiometric profiles were used to calculate attenuation coefficients for downwelling irradiance (Kd) and 

upwelling radiance (Ku), and surface values of Ed and Lu (Ed0- and Lu0-

, respectively) by simple linear 

regression. Instrument self-shading corrections were applied to each Lu0- according to Gordon and Ding 

(1992). 

In 2001 we expanded our sampling protocol to include two new radiometric instruments, the Satlantic

MicroPro free-fall radiometer and the Satlantic Hyperspectral Tethered Spectral Radiometer Buoy (HyperTSRB). The MicroPro is designed for work in more turbid waters with a small sensor cross-section, and is 

sufficiently compact to allow deployment from small boats. The Hyper-TSRB measures surface Ed and Lu

with a 120-channel detector from 400 to 800 nm. On each cruise in CB, sequential radiometric profiles 

were made with the MER (typically one cast) and MicroPro (2 to 5 casts). All casts were completed within

~5 to 8 minutes, depending on the number of MicroPro casts that were made. The TSRB was deployed 

prior to the radiometric profiles and continuously operated while on station. Since 2001 we also have been 

using a MicroTops sun photometer from the SIMBIOS instrument pool to measure aerosol optical

thickness. 

In 2002 and 2003 we participated in six EPA/NASA Atlantic Coast Estuaries Indicators Consortium

(ACE INC) cruises that sampled two tributaries of CB, the Patuxent and Choptank Rivers. ACE INC

cruises collected the same suite of measurements as the LMER-TIES and Biocomplexity cruises, with the 

exception that radiometric profiles of Ed and Lu were limited to the MicroPro because of vessel limitations. 

We have submitted over 3000 in-situ measurements to SeaBASS, significantly expanding bio-optical 

data for Case 2 waters (Table 7.1). Data from summer and fall cruises of 2003 will be submitted prior to the 

end of this contract. We also participated in the CHORS/Horn Point Laboratory HPLC Round Robin. We

worked together with Jason Perl to identify and minimize methodological and instrument differences 

between laboratories such that an internally consistent pigment database could be created. 

Algorithm Evaluation/Development 

We have evaluated the performance of the empirical algorithm OC4v.4 (O’Reilly et al., 1998) and the 

semi-analytical Garver-Siegel-Maritorena (GSM01) model (Maritorena et al., 2002) for the CB/MAB,

using bio-optical data collected during from SIMBIOS. This entailed using regional bio-optical data as 

input parameters to GSM01, specifically the chl a-normalized absorption, aph*(λ), and spectral slope of the 

combined absorption of dissolved and non-pigmented particulate matter, Scdm, to produce a regionally tuned 

version of the model that we termed GSM01-CB. We subsequently modified the native GSM01 parameters 

in the SeaWiFS Data Analysis System (SeaDAS) code to evaluate the performance of the GSM01-CB 

using satellite-derived radiances as input. 

 

SeaWiFS Image Analysis

We have continued acquisition and analysis of SeaWiFS ocean color imagery from the Goddard 

Distributed Active Archive Center (DAAC) for CB/MAB. Approximately 100 clear images are available 

each year fro 1998-present. We have used in-situ data to validate SeaWiFS performance in match-up 

analyses and comparisons with continuous underway measurements. A three-year times series (1998-2000) 

has been used to determine the extent to which SeaWiFS resolves seasonal and interannual variability of 

chl a evident in the in-situ data. 

7.3 RESEARCH RESULTS 

Radiometric Comparisons 

Expansion of our radiometric measurement suite allowed us to evaluate performance of each profiling 

instrument in nearly simultaneous (i.e., within minutes) deployments. The MicroPro is more suitable for 

work in Case 2 waters than the MER due to: (1) smaller diameter (MicroPro 4.8 cm relative to the MER 21

cm) that reduces instrument self-shading; (2) higher sampling frequency that gives greater depth resolution 

in waters where Kd is high and slight changes in the depth of the sensor have a larger effect on the 

measurements. The MicroPro exhibited clear advantages in waters with higher Kd. The improved depth 

85 
SIMBIOS Project Annual Report 

Table 7.1: Status of the Chesapeake Bay data set submitted to SeaBASS. Numbers for 2003 are estimated 

based on cruises yet to be completed. 

Direct Measurements 

Ed(z,λ) Downwelling irradiance depth profiles 

Lu(z,λ) Upwelling radiance depth profiles 

Es(0+

,λ) Incident irradiance 

ap(z,λ) Total particulate absorption (300-900 nm) of discrete samples by QFT 

ad(z,λ) Non-pigmented particulate absorption (300-900 nm) of discrete samples by

QFT with MeOH extraction 

acdom(z,λ) Colored dissolved absorption (190-820 nm) of discrete samples 

Chl a (z) Determination of chl a for discrete samples by Turner fluorometry

pigments (z) HPLC analysis for a suite of pigments 

T(z), S(z) SeaBird temperature and conductivity profiles 

Derived Products 

RRS(0+,λ) Remote sensing reflectance from calculated from radiance/irradiance 

profiles 

Kd (m,λ), Ku (m,λ) Attenuation coefficients for Ed(z,λ) and Lu(z,λ) for the surface mixed layer, 

calculated from radiance/irradiance profiles 

aph(z,λ) Pigmented particulate absorption (300-900 nm), calculated as ap(0,λ)- 

ad(0,λ)

Direct 

Measurements 

Derived 

Products 1996 1997 1998 1999 2000 2001 2002 2003

MER *

Ed(z,λ) 

Lu(z,λ) 

Es(0+

,λ) 

RRS(0+

,λ) 

Kd (m,λ) 

Ku (m,λ)

82 93 83 44 40 25 39 25

MicroPro **

Ed(z,λ) 

Lu(z,λ) 

Es(0+

,λ)

RRS(0+

,λ) 

Kd (m,λ) 

Ku (m,λ) 

--- --- --- --- --- 49 130 90

ap(0,λ), ad(λ) aph(λ) 79 32 130 66 46 27 32 25

aCDOM(0,λ) 79 32 130 66 46 27 32 25

Chl a (z) 323 709 591 514 330 189 182 60

Pigments (z) 143 95 104 63 56 49 32 20

T(z), S(z) --- 30 55 49 43 26 31 20

MicroTops --- --- --- --- --- --- 24 15

* MER wavebands 412, 443, 455, 490, 510, 532, 550, 650, 589, 625, 671, 683, 700 

**MicroPro wavebands 400, 412, 443, 455, 490, 510, 532, 555, 565, 590, 625, 670, 684, 700. 

86 
SIMBIOS Project Annual Report 

Figure 7.1: Comparisons of in-situ chl a to: (a) mean chl a estimated from OC4v.4 applied in-situ RRS; (b) 

SeaWiFS retrievals of chl a. SeaWiFS values represent the mean of a 3x3 pixel box centered on the station 

location for in-situ sampling. A chl a match-up was considered valid if the satellite observation and in-situ

measurement occurred on the same day, and half (5 out of 9) pixels in a box centered on the latitude and 

longitude of the sampling station returned valid estimates. The original match-up dataset contained over 

1400 chl a observations. Mean ratio = SeaWiFS chl a / in-situ chl a. 

87 
SIMBIOS Project Annual Report 

Figure 7.2: RRS555 and chl a from SeaWiFS for a CB scene, 15 October 1999. The white lines 

superimposed on these images give the ship track for concurrent measurements that included bio-optical

measurements. Vertical line at ~ 150 km marks the time of the satellite pass. 

resolution of the MicroPro (~25 measurements per meter) compared to the MER (~10 measurements per 

meter) compensated for the shallow optical depths in CB where the depth range available for the 

extrapolation interval was limited to the upper 4 m (or less). Measurements of Ed0+

 from the MicroPro were 

much easier to reconcile with surface irradiance measurements (Es) than were those from the MER, which 

exhibited an increasing bias as Kd increased. Measurements of Lu0- from the MER and MicroPro also 

showed increased disagreement as Kd increased. Based on advantages of the MicroPro over the MER in 

comparisons of Ed0+ and Es, we feel confident that the MicroPro measurements of Lu0-

 are more accurate 

than the MER for the Case 2 waters in which we work. Lastly, the increased depth resolution of the 

MicroPro reduced the effects of small changes in the selection of the depth interval on the calculated Kd, 

Ku, Ed0-, and Lu0-. The MicroPro also allowed us to expand our radiometric sampling to more turbid waters 

of the tributaries, and has improved measurement capabilities in the upper reaches of CB. In addition, these 

comparisons of the MER and MicroPro indicated strong agreement in the clearer waters of the mesohaline 

and polyhaline CB, confirming confidence in our multi-year time series of radiometric measurements with

the MER in the CB/MAB. 

88 
SIMBIOS Project Annual Report 

Figure 7.3: (a) Comparison of in-situ and SeaWiFS chl a; (b) SeaWiFS RRS510 and RRS555; (c) ratio of 

SeaWiFS RRS 510/555 along a ship track in the mesohaline to polyhaline CB.

Characterization of Bio-optical Properties in Chesapeake Bay 

Distributions of inherent optical properties in CB/MAB showed the strong influence of freshwater flow 

that complicates retrievals of chl a from satellite ocean color measurements. aph(440), aCDOM(440), ad(440) 

ranged from 0.1 to 1.0 m-1 in CB, and were an order of magnitude lower in the MAB. Whereas ad(440) 

dominated in upper CB, the magnitudes of aph(440), aCDOM(440), ad(440) were comparable in the mid- and 

lower CB and inshore MAB. aCDOM(440) dominated water column absorption in the offshore MAB. The 

distribution aCDOM(440) was conservative in CB proper, due to its predominant terrestrial origin. aph(440) 

and ad(440) were subject to much higher spatial variability. The distribution of dissolved and particulate 

materials regulated the magnitude and spectral shape of apparent optical properties such as the attenuation 

coefficient for downwelling irradiance, Kd, and the remote-sensing reflectance, RRS. 

 

OC4v.4 Algorithm Evaluation 

The distribution of inherent optical properties had a pronounced effect on the performance of OC4v.4 

in CB/MAB (Fig. 7.1a). OC4v.4 returned chl a estimates for CB that had a small positive bias and high

uncertainty, reflecting that aph, aCDOM, ad did not co-vary. A larger positive bias of chl a occurred in the 

MAB where too much of the total absorption was attributed to phytoplankton. Match-ups of SeaWiFS chl a

with in-situ data showed a larger positive bias in CB than observed for OC4v.4 applied to in-situ

radiometric data, suggesting SeaWiFS-derived radiances introduced additional errors (Fig. 7.1b). Whereas 

the fourth SeaWiFS reprocessing lessened the frequency of negative LWN, comparisons of in-situ and 

satellite-derived LWN as part of this work and others have shown that low and negative LWN from

SeaWiFS remain a problem in coastal and estuarine waters (Harding et al., 2003; O’Reilly and Yoder, 

2003). SeaWiFS LWN at ‘blue’ wavebands are low in CB, causing the return of higher-than-observed chl a 

89 
SIMBIOS Project Annual Report 

values by OC4v.4. The lack of increased bias in chl a for match-ups from the MAB suggested errors in

SeaWiFS chl a could be attributed primarily to OC4v.4 rather than errors in atmospheric correction. 

Resolution of Interannual, Seasonal and Event-scale Variability by SeaWiFS 

High uncertainties we detected in match-up analyses of in-situ and SeaWiFS chl a restricted the 

usefulness of SeaWiFS data for CB/MAB, stimulating additional analyses to lessen time-space aliasing we 

believed was responsible. High frequency and small-scale variability of biomass typical of estuaries 

virtually assures time-space aliasing of satellite observations and in-situ measurements. To minimize 

aliasing, we compared continuous shipboard measurements of chl a were to SeaWiFS chl a. along ship

tracks to evaluate the extent to which SeaWiFS resolved variability on small spatial scales. We found 

SeaWiFS accurately captured small-scale (tens-of-kilometers) variability in the mesohaline and polyhaline 

regions of CB (Figs. 7.2 and 7.3) (see Harding et al., 2003). Increased noise in chl a with distance north 

corresponded to sharp gradients of RRS. We suspect that the magnitude of the RRS signal often approaches 

the limits of sensitivity for the SeaWiFS sensor in turbid waters with high Kd typical of mesohaline and 

oligohaline CB, thereby limiting the accuracy of chl a retrievals. This gradient of RRS was persistent in the 

imagery and was suggested in field radiometric measurements. Comparisons of several transects suggested 

axial movement of this gradient of RRS could determine the areal extent of accurate chl a retrievals. 

Our evaluation of the 1998 – 2000 SeaWiFS chl a time series demonstrated that SeaWiFS accurately 

represented seasonal and interannual variability of chl a revealed by in-situ data (Fig. 7.4). Previous 

research has shown that the timing, position, and magnitude of the spring bloom are regulated by

freshwater flow from the Susquehanna River, supplying nutrients and suspended matter to CB (Malone et 

al., 1988; Harding, 1994). We found that positive deviations of flow in 1998 resulted in a large spring 

bloom that extended to the polyhaline CB, shown in both satellite imagery and in-situ data. Measurements 

of chl a in 1999, a year of lower-than-average freshwater flow, showed a smaller bloom that was confined 

primarily to the mesohaline CB. Lastly, average freshwater flow in 2000 supported a bloom of intermediate

magnitude that extended throughout the mesohaline and part of the polyhaline CB. As observed in transect

data, the agreement of SeaWiFS and in-situ chl a improved with distance down the Bay. These 

comparisons with in-situ data confirm that SeaWiFS chl a retrievals are reasonable and can significantly 

augment the temporal resolution of shipboard measurements. 

The benefit of increased temporal resolution afforded by satellite observations was demonstrated in the 

three-year time series of chl a (Fig. 7.5). Mean, daily chl a for a section of polyhaline CB indicated several

doublings over a period of days in summer that were not captured by in-situ observations. We hypothesize 

that these doublings could be associated with short-lived, but dense blooms of dinoflagellates that have 

been observed before during stratified conditions in summer. The sedimentation of organic matter from the 

spring bloom fuels at least 50% of the primary productivity in surface waters (Kemp and Boynton, 1984). 

Transient destratification of the summer pycnocline provides the nutrients to support these short-lived 

summer blooms (Malone et al., 1986, 1988). The higher frequency of these events in 1998 and 2000, 

contrasted with 1999, may indicate a connection between these transient blooms in summer and the 

magnitude of the spring bloom. Further study is needed to test this hypothesis. 

Regional Parameterization of GSM01 

Semi-analytical models such as GSM01 have the potential to distinguish multiple constituents of the 

water column that absorb and scatter light. This attribute is crucial for accurate retrievals of chl a in

optically-complex waters. The original parameterization of GSM01 was based on in-situ observations from

Case 1 waters and optimized for performance in the global ocean (Maritorena et al., 2002). We

hypothesized that regional parameterization was necessary to improve the accuracy of chl a retrievals in 

CB/MAB. 

We evaluated our extensive data set of in-situ observations to derive regionally and seasonally (when 

appropriate) values for aph*(λ) and Scdm to produce a regional version of the model, GSM01-CB (Table

7.2) (see Magnuson et al., 2003). Regional differences of aph*(λ) were significant across the study area 

from the upper, oligohaline CB to the offshore MAB. aph*(λ) increased with distance offshore. This trend 

of increasing aph*(λ) can be explained by two factors: decreases of pigment packaging and increases in 

concentrations of accessory pigments (Yentsch and Phinney, 1989; Bricaud et al., 1995). 

90 
SIMBIOS Project Annual Report 

Figure 7.4: Monthly mean chl a for the polyhaline (I, II, south of 37.8°N) and mesohaline (III, IV, 37.8 – 

38.8°N) regions of CB from in situ (closed symbols) and SeaWiFS (open symbols) data for 1998-2000. 

We also found significant seasonal differences in mean aph*(λ) in CB that we attributed primarily to

changes in floral composition and associated changes in pigment packaging. Highest aph*(λ) occurred in 

spring when phytoplankton in CB consist primarily of large cells (i.e., centric diatoms) (Malone et al., 

1988; Marshall and Nesius, 1996). Lowest aph*(λ) occurred in summer when phytoplankton consist of a 

more diverse flora of smaller, flagellated cells (Malone et al., 1991; Marshall and Nesius, 1996). Scdm

exhibited an increasing trend with distance offshore, reflecting the decrease in the influence of terrigenous 

CDOM (Blough et al., 1993; Nelson and Guarda, 1995; Vodecek et al., 1997). Small, but significant

differences in Scdm along the axis of CB reflected changes in the relative contributions of aCDOM and ad to

acdm. 

Evaluation of chl a from OC4v.4, GSM01, and GSM01-CB for CB, inshore MAB, and offshore MAB

showed that GSM01-CB improved upon OC4v.4 in all regions (Table 7.3). 

91 
SIMBIOS Project Annual Report 

Figure 7.5: Time-series of mean, daily SeaWiFS (open symbols) and in-situ (closed symbols) chl a for a 

section of CB from 37.3° to 37.8°N. 

92 
SIMBIOS Project Annual Report 

Figure 7.6: Histograms of in-situ and SeaWiFS chl a observations for CB, inshore and offshore MAB for 

image from 4 May 2000: (a-c) in-situ, (d-f) SeaWiFS OC4v.4, (g-i) SeaWiFS GSM01, and (j-l) SeaWiFS 

GSM01-CB. In-situ data from the MAB were not available for this image. The inshore and offshore MAB 

histograms (b-c) reflect all field data available for those regions during May-September 1996-1998. 

93 
SIMBIOS Project Annual Report 

Table 7.2. Parameter values used in GSM01 and GSM01-CB.

Region Season a (412) * aph (443) * aph (490) * aph (510) * ph* aph (671) * Scdm η

GSM01 0.00665 0.05582 0.02055 0.01910 0.01015 0.01424 0.02061

GSM01-CB 

Spring 0.02119 0.02509 0.01282 0.00910 0.00427 0.01218 0

Summer 0.02653 0.02979 0.01655 0.01208 0.02122 0.01218 0 Upper 

Bay

Fall 0.02259 0.02573 0.01372 0.00376 0.02066 0.01218 0

Spring 0.02212 0.01279 0.00974 0.00449 0.01588 0.01385 0

0.03345 0.03900 0.02318 0.01664 0.00609 0.02285 0.01385 0 MidBay

Fall

ph a (555)

1.03373

0.02087

0.00470

0.01019

0.02001

Summer

0.02758 0.03080 0.01826 0.01438 0.00691 0.02112 0.01385 0

Spring 0.02001 0.02212 0.01279 0.00974 0.00449 0.01588 0.01330

Summer 0.03345 0.03900 0.02318 0.01664 0.00609 0.02285 0.01330 0 Lower 

Bay

Fall 0.02758 0.03080 0.01826 0.01438 0.00691 0.02112 0.01330 0

Inshore 0.07123 0.08843 0.06024 0.04072 0.01693 0.03815 0.01236 0

Offshore 0.11331 0.14678 0.09832 0.06048 0.01920 0.04349 0.01646 1

0

Table 7.3: Comparison of the mean ratio (±SD) of modeled and measured chl a and acdm (443), and 

estimated bbp (443) for Chesapeake Bay, inshore MAB, and offshore MAB. Mean ratio = modled/measured 

Chesapeake Bay Chl a Inshore MAB Chl a Offshore MAB Chl a 

GSMO1 1.99 ± 2.21 1.84 ± 0.92 1.31 ± 0.68 

GSM01-BAY 1.10 ± 1.08 0.59 ± 0.57 0.51 ± 0.29 

OC4v.4 1.20 ± 0.71 3.12 ± 2.05 2.40 ± 1.03 

Chesapeake Bay acdm (443) Inshore MAB acdm (443) Offshore MAB acdm (443) 

GSMO1 1.24 ± 0.58 1.01 ± 0.54 0.87 ± 0.61 

GSM01-BAY 0.92 ± 0.40 1.12 ± 0.42 0.95 ± 0.67 

Chesapeake Bay bbp (443) Inshore MAB bbp (443) Offshore MAB bbp (443) 

GSMO1 0.94 ± 0.65 2.28 ± 1.35 1.43 ± 0.88 

GSM01-BAY 1.35 ± 0.91 2.88 ± 1.64 1.34 ± 1.26 

The local parameterization was essential to improve performance in CB. Both GSM01 and GSM01-CB

showed improvement over OC4v.4 in the MAB where they specifically accounted for high absorption due 

to CDOM. Analysis of SeaWiFS products using each model supported results of evaluations using in-situ

RRS as inputs, specifically that: (1) one or both of the semi-analytical models performed as well or better 

than OC4v.4; (2) both semi-analytical models clearly improved upon OC4v.4 in the offshore MAB (Fig. 

7.6). Disagreement between the in-situ observations and model estimates for the inshore MAB likely reflect 

a sampling bias as in-situ observations were concentrated near the Chesapeake and Delaware Bay mouths 

where chl a are generally high, whereas mean chl a from SeaWiFS represented the entire inshore region. 

94 
SIMBIOS Project Annual Report 

Disregarding in-situ values, both semi-analytical models returned much lower mean chl a for the inshore 

MAB than did OC4v.4, and GSM01-CB chl a were lower than GSM01 chl a, consistent with results 

obtained using in-situ data as input to the models (Table 7.3). The semi-analytical models return estimates 

of acdm(443) and bbp(443) in addition to chl a. Estimates of acdm(443) were closer to in-situ values than were 

estimates of chl a (Table 7.3). Meaningful evaluation of the bbp(443) product was not possible due to the 

lack of direct measurements of scattering. Relatively high uncertainties remained for all models, reflecting

the difficulty of working in bio-optically complex waters. It is notable that the performance of GSM01 was 

surprisingly good, considering the parameterizations differed significantly from bio-optical properties of 

CB. The relatively strong performance of GSM01 in CB likely reflects the robust tuning procedures used 

by Maritorena et al. (2002) to derive global parameters for GSM01. 

7.4 CONCLUSIONS 

It is useful and necessary to assess the performance of satellite products at various stages to recognize 

limitations, as well as to demonstrate strengths and identify the types of applications for which current

products are suitable. We have made use of an extensive database of bio-optical measurements to provide 

several different evaluations of SeaWiFS performance. We have identified several factors that inhibit the 

accuracy of the standard chl a product, including: (1) use of an empirical algorithm that does not account 

for all of the absorbing and scattering components of the water column; (2) atmospheric correction errors in

coastal waters resulting in low satellite radiances; (3) RRS signals approaching the lower sensitivity limits 

of the sensor. Nonetheless, the importance of these factors lessened, and SeaWiFS recoveries improved, 

with increasing water clarity. We found that in mesohaline and polyhaline CB, SeaWiFS accurately and 

reliably represented seasonal and interannual variability of biomass, resolved event-scale phenomena that 

were missed in our field sampling, and resolved small-scale (i.e., tens-of-kilometers) regional variability. 

We are encouraged by our initial attempts to improve chl a estimates by tuning a semi-analytical model for 

regional conditions. We are exploring modifications to the parameterization that could lessen the use of 

abrupt transitions in parameter values at seasonal and regional boundaries. Improvements in SeaWiFS 

radiances also will be necessary for maximal performance of semi-analytical models in Case 2 waters. 

REFERENCES 

Blough, N.V., O.C. Zafirou, and J. Bonilla, 1993: Optical absorption spectra of waters from the Orinoco 

River outflow: terrestrial input of colored organic matter to the Caribbean. Journal of Geophysical

Research 98, 2,271-2,278. 

Bricaud, A., M. Babin, A. Morel, & H. Claustre, 1995: Variability in the chlorophyll-specific absorption 

coefficients of natural phytoplankton: analysis and parameterization, Journal of Geophysical Research

100, 13,321-13,332. 

Gordon, H.R. and K. Ding. 1992. Self-shading of in-water optical instruments. Limnology and 

Oceanography., 37, 491-500. 

Kemp, W.M., & W.R. Boynton, 1984: Spatial and temporal coupling of nutrient inputs to estuarine primary

production: the role of particulate transport and decomposition, Bulletin of Marine Science ,35, 242-

247. 

Harding, L.W., Jr., E.C. Itsweire, and W.E. Esaias, 1994: Estimates of phytoplankton biomass in the 

Chesapeake Bay from aircraft remote sensing of chl-a, 1989-1992, Remote Sensing of Environment

,49, 41-56. 

Harding, L.W., Jr., A. Magnuson, and M.E. Mallonee, 2003: Bio-Optical and Remote Sensing 

Observations in Chesapeake Bay, Estuarine, Coastal, and Shelf Science (in review). 

95 
SIMBIOS Project Annual Report 

Magnuson, A., L.W. Harding, Jr., M.E. Mallonee, and J.E. Adolf, 2003: Bio-optical modeling in the 

Middle Atlantic Bight, Estuarine, Coastal, and Shelf Science (to be submitted). 

Malone, T.C., W.M. Kemp, H.W. Ducklow, W.R. Boynton, J.H. Tuttle, and R.B. Jonas, 1986: Lateral 

variation in the production and fate of phytoplankton in a partially stratified estuary, Marine Ecology 

Progress Series , 32, 149-160. 

Malone, T.C., L.H. Crocker, S.E. Pike, and B.W. Wendler, 1988: Influences of river flow on the dynamics 

of phytoplankton production in a partially stratified estuary, Marine Ecology Progress Series ,48, 235-

249. 

Malone, T.C., H.W. Ducklow, E.R. Peele, and S.E. Pike, 1991: Picoplankton carbon flux in Chesapeake 

Bay, Marine Ecology Progress Series, 78, 11-22. 

Maritorena, S., D.A Siegel, and A.R. Peterson, 2002: Optimization of a semianalytical ocean color model 

for global-scale applications, Applied Optics, 41, 2,705-2,714. 

Marshall, H.G. and K.K. Nesius, 1996: Phytoplankton composition in relation to primary production in 

Chesapeake Bay, Marine Biology, 125, 611-617. 

Nelson, J.R. and S. Guarda, S. 1995: Particulate and dissolved spectral absorption on the continental shelf 

of the southeastern United States, Journal of Geophysical Research 100, 8,715-8,732. 

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 algorithms for SeaWiFS, Journal of Geophysical Research ,103, 24,937-

24,953. 

O’Reilly, J.E. and J.A. Yoder, 2003: A comparison of SeaWiFS LAC Products from the Third and Fourth 

Reprocessing: Northeast US Ecosystem, in Algorithm Updates for the Fourth SeaWiFS Data

Reprocessing (S.B. Hooker & E.R. Firestone, eds). NASA Tech. Memo. 2003-206892, NASA Goddard 

Space Flight Center, Greenbelt, MD, pp. 60-67. 

Vodecek, A., N.V. Blough, M.D. DeGrandpre, E.T. Peltzer, and R.K. Nelson, 1997: Seasonal variation of 

CDOM and DOC in the Middle Atlantic Bight: Terrestrial inputs and photooxidation, Limnology and 

Oceanography ,42, 674-686. 

Yentsch, C.S. and D.A. Phinney, 1989: A bridge between ocean optics and microbial ecology, Limnology 

and Oceanography, 34, 1,694-1,705. 

This Research was Supported by the 

 NASA Contract # 00195 

Publications 

Harding, L.W., W. . Miller, R.N. Swift, and C.W. Wright, 2001: Aircraft remote sensing, In: Steele, J., S. 

Thorpe, and K. Turekian, (eds.), Encyclopedia of Ocean Sciences, Academic Press, London, UK, 113-

122. 

Harding, L.W., Jr., M.E. Mallonee, and E.S. Perry, 2002: Toward a predictive understanding of primary

productivity in a temperate, partially stratified estuary, Estuarine, Coastal and Shelf Science, 55, 437-

463. 

Harding, L.W., Jr., A. Magnuson, and M.E. Mallonee, 2003: Bio-Optical and remote sensing observations 

in Chesapeake Bay, Estuarine, Coastal, and Shelf Science (in review). 

96 
SIMBIOS Project Annual Report 

Magnuson, A., L.W. Harding, Jr., M.E. Mallonee, and J.E. Adolf, 2003: Bio-optical modeling in the middle 

Atlantic bight, Estuarine, Coastal, and Shelf Science (to be submitted). 

Presentations 

Harding, L.W., Jr., Seasonal and inter-annual variability of primary productivity in Chesapeake Bay from

remotely sensed aircraft observations, at Phytoplankton Productivity - An Appreciation of 50 Years of 

the Study of Production in Oceans and Lakes, 19 March 2002, Bangor, Wales, United Kingdom. 

Magnuson, A., L.W. Harding, Jr., and M.E. Mallonee, Progress toward a case II bio-optical algorithm

optimized for Chesapeake Bay and the adjacent waters of the mid-Atlantic bight. Ocean Optics XVI, 

18-22 November 2002, Sante Fe, NM. 

Magnuson, A., L.W. Harding, Jr., M.E. Mallonee, and W.D. Miller, Comparison of In Situ radiometric 

measurements in turbid waters, poster presented at the 5th SIMBIOS Science Team Meeting, 15-17 

January 2002, Baltimore, MD. 

 

 

 

 

 

 

97 
SIMBIOS Project Annual Report 

Chapter 8 

Refinement of Protocols for Measuring the Apparent 

Optical Properties of Seawater 

Stanford B. Hooker 

NASA/GSFC Greenbelt, Maryland 

Giuseppe Zibordi and Jean-François Berthon 

JRC/IES/Inland and Marine Waters, Ispra, Italy 

André Morel and David Antoine 

CNRS/UPMC/Laboratoire d’Océanographie de Villefranche, Villefranche-sur-Mer, 

France 

8.1 INTRODUCTION 

Ocean color satellite missions, like the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) or the 

Moderate Resolution Imaging Spectroradiometer (MODIS) projects, are tasked with acquiring a global

ocean color data set, validating and monitoring the accuracy and quality of the data, processing the 

radiometric data into geophysical units using a set of atmospheric and bio-optical algorithms, and 

distributing the final products to the scientific community. The long-standing requirement of the SeaWiFS 

Project, for example, is to produce spectral water-leaving radiances, LW(λ), to within 5% absolute (λ

denotes wavelength) and chlorophyll a concentrations to within 35% (Hooker and Esaias 1993), and most

ocean color sensors have the same or similar requirements. Although a diverse set of activities are required 

to ensure the accuracy requirements are met (Hooker and McClain 2000), the perspective here is with field 

observations. Assuming half of the total uncertainty budget is apportioned to the satellite sensor and that 

the uncertainties sum in quadrature (the square root of the sum of the squares), the allowed uncertainty in

the in situ data is approximately 3.5% ( 2 5 / 2 ). 

The accurate determination of upper ocean apparent optical properties (AOPs) is essential for the 

vicarious calibration of ocean color data and the validation of the derived data products, because the seatruth measurements are used to evaluate the satellite observations (Hooker and McClain 2000). The 

uncertainties with in situ AOP measurements have various sources: a) the sampling procedures used in the 

field, including the environmental conditions encountered; b) the absolute characterization of the 

radiometers in the laboratory; c) the conversion of the light signals to geophysical units in a processing 

scheme, and d) the stability of the radiometers in the harsh environment they are subjected to during

transport and use. Assuming ideal environmental conditions, so this aspect can be neglected, the SeaWiFS 

ground-truth uncertainty budget can only be satisfied if each uncertainty is on the order of 1–2%, or what is 

generally referred to as 1% radiometry. 

In recent years, progress has been made in estimating the magnitude of some of these uncertainties and 

in defining procedures for minimizing them. For the SeaWiFS Project, the first step was to convene a 

workshop to draft the SeaWiFS Ocean Optics Protocols (hereafter referred to as the Protocols). The 

Protocols initially adhered to the Joint Global Ocean Flux Study (JGOFS) sampling procedures (JGOFS 

1991) and defined the standards for optical measurements to be used in SeaWiFS calibration and validation 

activities (Mueller and Austin 1992). Over time, the Protocols were revised (Mueller and Austin 1995), and 

then recurringly updated on essentially an annual basis (Mueller 2000, 2002, and 2003) as part of the 

Sensor Inter-comparison and Merger for Biological and Interdisciplinary Oceanic Studies (SIMBIOS) 

project. 

98 
SIMBIOS Project Annual Report 

This report summarizes advances in ocean optics protocols derived from a variety of inquiries brought 

to completion within the last year of the SIMBIOS project. The presentation is restricted to minimizing

instrument characterization, in situ sampling, and data processing uncertainties for AOP sensors normally

used in vicarious calibration activities. The full inquiry included the uncertainties associated with the 

determination of pigment concentrations using high performance liquid chromatography (HPLC), because 

pigment concentrations are an essential part of a complete validation program. The results for the latter are 

not described here, because they are not part of AOP investigations. The complete details, however, are 

presented in Claustre et al. (2003) and Hooker et al. (2003a). 

8.2 INSTRUMENT CHARACTERIZATION PROTOCOLS 

The immersion factor, If (λ), is a necessary part of the spectral characterization of an in-water 

irradiance sensor, because when a cosine collector is immersed in water, its light transmissivity is less than 

it was in air. Irradiance sensors are calibrated in air, however, so a correction for this change in collector 

transmissivity must be applied when the in-water raw data are converted to physical units. The immersion 

factor must be determined experimentally, using a laboratory protocol, for each collector. 

Depth (z) profiles of the downward and upward irradiances, Ed (z,λ) and Eu(z,λ), respectively, are 

directly influenced by uncertainties in the characterization of immersion factors. Although irradiances do 

not appear explicitly in the determination of LW (λ)—which is derived primarily from a profile of the 

upwelled radiance, Lu(z,λ)—vicarious calibration activities frequently rely on measurements, normalized

variables, or alternative methods for producing LW (λ) which require irradiance variables: 

1. The Q-factor, which requires Eu (z,λ) and Lu (z,λ), or the use of Q-factor look-up tables with

measurements of Eu (z,λ)to derive Lu (z,λ); 

2. The irradiance reflectance R(z,λ), when computed with Eu (z,λ) and Ed (z,λ) obtained from different

radiometers; 

3. The normalized water-leaving radiance, LWN (λ), as well as the remote sensing reflectance, Rrs(λ), 

when the solar irradiance, Ed (0+

,λ), is computed from subsurface Ed(0

-

,λ)values (0+ and 0-denote 

measurements immediately above and below the sea surface, respectively); 

4. Determination of the near-surface extrapolation interval, and thus the water-leaving radiance, for 

those processing schemes taking advantage of the convergence of Ed (0+

,λ) and Ed (0-,λ)to select 

the interval limits; and 

5. Derived quantities formed from band ratios of R (λ), LWN (λ), or Rrs(λ)at different wavelengths. 

The latter are the input variables for algorithms inverting the optical measurements to derive the 

chlorophyll a concentration (O’Reilly et al. 1998). 

Studies of immersion effects date back to the work of Atkins and Poole (1933), who attempted to

experimentally estimate the internal and external reflections for an opal glass diffuser. Additional

investigations by Berger (1958 and 1961) refined the laboratory procedures and Westlake (1965) gave 

detailed explanations for the internal and external reflection contributions. A comprehensive description of 

a protocol for a more modern Plexiglas diffuser was given by Smith (1969). The culmination of these early

investigations was the incremental method, or what is now referred to as the traditional method. 

The traditional method has been in use for the past 25 years, and originated with the protocol revisions 

suggested by Aas (1969) and communicated more widely by Petzold and Austin (1988). They all advocated 

using a lamp as a light source and including a geometric correction factor as a function of the lamp–

collector distance, incremental changes in the water depth, and the water refractive index. The advantage of 

the geometric correction factor is it minimizes the effects caused by changes in the flux reaching the 

collector as a function of the change in water depth. 

The traditional method involves a relatively simple procedure and a small number of components 

(Mueller and Austin 1995). A lamp of suitable wattage is needed to provide a flux of light at all sensor 

wavelengths well above dark values, with the appropriate baffing and apertures to minimize diffuse light

contributions into the tank. A lamp with a small filament is preferred, because it better approximates a point 

source, and a regulated power supply should be used to ensure the emitted flux from the lamp is stable over 

the time period of each characterization trial. The water vessel or tank should have a removable aperture 

99 
SIMBIOS Project Annual Report 

sized or adjusted to ensure the direct beam of light from the lamp projects onto an area that is only slightly

larger than the area of the diffuser. The interior of the tank must be flat black, and contain a sensor support

system that permits an accurate horizontal leveling of the diffuser and an accurate alignment of the sensor 

with respect to the centerline of the lamp filament. 

Two alternatives for characterizing immersion factors were recently evaluated by Hooker and Zibordi 

(2003a). For the first method, the optical measurements taken at discrete water depths are substituted by 

continuous profiles created by removing the water with a pump. In the second method, the commonly used 

large tank is replaced bya small water vessel with sidewall baffes, which permits the use of a qualityassured and reproducible volume of water. The latter was achieved by refining the capabilities of the 

Compact Portable Advanced Characterization Tank (ComPACT), which had already been built for experimenting with immersed sensors. 

The continuous method takes advantage of having a pump (with an almost constant discharge rate) to

drain the water vessel. For a cylindrical tank, the water depth can be approximated as a linear function of 

time as the water is pumped out. Consequently, there is no need to stop at discrete depths to record the 

diffuser measurements. This means the total characterization time is the time needed to prepare, fill, and

empty the tank (about 40 min for a large, 350 L, tank), which is considerably shorter than the traditional 

method (which usually requires about 100 min, but for some setups as much as 300 min). 

The ComPACT apparatus originated from a desire to perform quality tests on immersed radiometers 

either in the field (measuring the response of bio-fouled sensors while still wet) or in the laboratory (the 

characterization of immersion factors). The original concept was to have a small (portable) water vessel, 

with baffing elements to minimize light reflections inside the tank if an extended source was used for 

illumination. The basic elements of the Com-PACT measurement protocol involve many elements associated with the traditional method, e.g, leveling, aligning, and adjusting the components before the 

experimental process can be executed. These and many other finer levels of detail are not recounted here, 

because they are provided by Zibordi et al. (2003a). The primary advantages of the ComPACT apparatus in 

the characterization of immersion factors is a) it only requires 3 L of water, which means pure water, 

manufactured shortly before use, can be used, and b) the small size places minimal requirements on work 

spaces and waste water requirements. 

The validation of the continuous and ComPACT methodsis accomplished by comparing them to the 

traditional method. Specifically, the If (λ)values from an alternative method and a specific sensor (Y ) are 

differenced with respect to the If (λ)values for that sensor as provided by the traditional method (X). 

Relative percent difference (RPD) values, 

100 Y X

X φ − = (8.1) 

are used to evaluate the performance characteristics of the methods. The overall capabilities are derived by 

averaging across all sensors or all wavelengths (which are identical for all the sensors). 

The intercomparison of the alternative and traditional methods is given in Fig. 8.1. The results show a 

significant convergence of the two new methods with the traditional method with individual sensor 

differences generally well below 1%. The histogram of RPD values (inset panel) has a significant central 

peak, but a slightly distorted gaussian distribution—there is a small net positive bias; the average RPD is 

0.1%. The average repeatability for single-sensor characterizations (across seven wavelengths) of the three 

methods are very similar and approximately 0.5%. 

The evaluation of the continuous method demonstrates its full applicability in the determination of 

immersion factors with a significant time savings. The evaluation of the small water vessel demonstrates 

the possibility of significantly reducing the size of the tank (along with decreasing Fig. 8.1. A comparison 

of the traditional If (λ) method versus the continuous (open circles) and ComPACT (solid circles) methods. 

The experimental trials for the new methods were executed in as small a time difference with respect to the 

traditional method as possible. The sensors involved are the same ones characterized during the eighth 

SeaWiFS Intercalibration Round-Robin Experiment (SIRREX-8) for investigating uncertainties in the 

traditional method (Zibordi et al. 2003b). The sensors had identical (nominal) center wavelengths and were 

usually characterized more than once, although one of the radiometers was selected as a so-called reference 

sensor and was measured more frequently than the others. The mix of sensors used with the ComPACT 

method is different than those used with evaluating the continuous method, but five are common to both 

100 
SIMBIOS Project Annual Report 

and between the two types of experiments, all 12 of the sensors used during SIRREX-8 are represented. 

The anomalously low If (λ)value at approximately 1.275 is a confirmed feature of one of the sensors. The 

inset panel shows the histogram of RPD values, with the traditional method used as the reference in the 

RPD calculation. 

Figure 8.1:A comparison of the traditional If (λ) method the continuous (open circles) and ComPACT 

(solid circles) methods. The experimental trials for the new methods were executed in as small a time 

difference with respect to the traditional method as possible. The sensors involved are the same ones 

characterized during the eight SeaWiFS Intercalibration Round Robin Experiments (SIRREX-8) for 

investigating uncertainties in the traditional method (Zibordi et al. 2003b). The sensors had identical

(nominal) center wavelengths and were usually characterized more than once, although one of the 

radiometers was selected as a so-called reference sensor and was measured more frequently than others. 

The mix of sensors used with the ComPACT method is different than those used with evaluating the 

continuous method, but five are common to both and between the two types of exeperiments, all 12 of the 

sensors used during SIRREX-8 are represented. The inset panel shows the histogram of RPD values, with

the traditional method used as the reference in the RPD calculation. 

101 
SIMBIOS Project Annual Report 

the execution time) and permitting a completely reproducible methodology (based on the use of pure 

water). The results also show sidewall reflections can be properly minimized with internal baffes. Within

the context of experimental effciency and reproducibility, this study suggests the combination of a properly 

baffed small tank with a constant flow pump would be an optimal system. 

8.3 IN SITU SAMPLING PROTOCOLS 

Achieving working at the 1% level is achievable in the laboratory, doing so in the field is much more 

diffcult, which is well demonstrated by considering the magnitude of the perturbations in the proximity of a 

large structure as a specific example. This is an appropriate choice, because platform effects are a recurring 

problem for in situ optical methods. These perturbations are made more complex according to the sun 

orientation with respect to the structure, and differentially influence the data obtained by above-and inwater methods. For example, from the perspective of the in-water light field, investigations within 15–20 m

of an offshore tower show significant effects of the structure: approximately 3–8% for clear-sky conditions, 

and as much as 20% under overcast conditions (Zibordi et al. 1999). Similar uncertainties have been 

estimated for in-water measurements from a ship (Voss et al. 1986). 

From a measurement perspective, the above-water approach is more restrictive, because there is 

presently no reliable mechanism for floating an above-water system away from a platform (which is easily

and effectively accomplished for an in-water system), so all above-water measurements are made in close 

proximity to a large structure. For the purposes of the results presented here, the proximity of the sampling 

platform is parameterized as the perpendicular distance x from the side of the sampling platform to the 

center of the area on the sea surface observed by the sea-viewing sensor, the so-called surface spot. The 

possible values of x range from approximately 0 (the field of view of the sensor must view only water and 

no part of the sampling platform), to a maximum value approximately equal to the height of the sensor 

above the water, H. 

The team assembled to study platform perturbations imagined a horizontal deployment system (HDS) 

that would be easy to operate by one person, and modular for conventional transportation to the sampling 

platform. The idea wastobe able to extend an above-water system about 10 m away from the platform. The 

above-water instruments commonly in use are too large to safely use such a system on a moving platform, 

like a ship. The unique stability of a tower offered the best opportunity to satisfy all the design 

requirements, but only if the size of the radiometers could be significantly reduced. Prior to the initial 

design of the HDS, there was already a concerted effort to produce smaller and lighter sensors for SeaWiFS 

field campaigns. A next-generation (very small) version of the SeaWiFS Surface Acquisition System, 

called microSAS, was available for testing at the same time when the HDS design was being initiated, so

this new sensor system was used to set the design criteria for the HDS (Hooker et al. 2003b). 

The microSAS system measures the radiances required for the above-water approach used here, the socalled modified Fresnel reflectance glint correction as presented in the version 1 revision of the Protocols 

(Mueller and Austin 1995), hereafter referred to as S95. This method assumes the total radiance measured 

at the sea surface, LT (λ), is a combination of LW (λ) plus two sources of reflected light or glint: the sky and

the sun. If the latter is minimized by pointing the measurement instruments at least 90◦ away from the sun 

plane (but not into any perturbations associated with the platform), the only quantity needed to retrieve LW

(λ) from LT (λ)isan estimate of the sky radiance, Li(λ), contribution. Removing the sky glint also requires an 

estimate of the surface reflectance, ρ.In the original formulation of S95, ρ wasa constant, but more realistic

values for ρ as a function of the viewing geometry and the wind speed are available from Mobley (1999). 

The incorporation of the latter is hereafter referred to as the S01 method (Hooker et al. 2003b): 

01 ˆ ( ) ( ) ( ) ( ), S LwW T λ = − L λ ρ W Li λ (8.2) 

where the pointing angles are omitted for brevity. 

The tower-perturbation field measurements were carried out on the Acqua Alta Oceanographic Tower 

(AAOT) and within the framework of the Coastal Atmosphere and Sea Time Series (CoASTS) Project 

(Zibordi et al. 2002a). The tower is located in the northern Adriatic Sea approximately 15 km offshore of 

102 
SIMBIOS Project Annual Report 

the Venice Lagoon in a frontal region that can be characterized by Case-1 or Case-2 conditions, although 

the former predominate (Berthon et al. 2002). The HDS was mounted on the topmost level (Fig. 8.2), and 

consists of a tubular horizontal mast sliding between rollers mounted within five rigid support frames. The 

HDS can carry an instrument package weighing approximately 10 kg, and to deploy it up to as much as 12 

m away from the tower with a vertical deflection of the mast less than 1% (Van der Linde 2003). Although 

the HDS can be moved an arbitrary distance, 10 distance or mast index markers were placed on the 

horizontal mast in 1 m intervals. A mast index origin was established at the first mast support, so a quick 

determination of the relative position of the mast with respect to the tower could be determined (and

reliably reproduced). 

Following the work by Hooker and Morel (2003), the presence of an artificial perturbation in an abovewater measurement can be detected (after wave effects have been removed) with the ratio

 

(865)/ (865) (865) ( )L L T i r

ρ W = (8.3) 

 

where the numerator comes from the Morel (1980) assumption that the sea surface is essentially black at a 

near-infrared wavelength (i.e., the above-water radiance measured is entirely due to surface reflection, 

principally from sky radiation once the sensor is pointed at least 90◦ away from the sun), and the 

denominator is the modeled surface reflectance from Mobley (1999). Under natural circumstances (i.e., in 

the absence of platform perturbations) and in Case-1 water conditions, ρ (W )= LT (865)/Li (865), within the 

accepted variance, and r (x, 865) = 1. Any other reflected radiation added to the sky-reflected radiance leads 

to an increase in LT (x, 865), and r (x, 865) > 1. 

Fig. 8.2. Aschematic of the top view of the AAOT. The HDS is installed along the northwest edge and 

extends over the flying bridge that projects out over the main body of the tower. The microSAS sensors are 

mounted in a cardanic gimbal whose axes are aligned in the direction of, and perpendicular to, the HDS 

mast. Sensors inside the gimbal ballast confirm the azimuthal angle with respect to the solar azimuth, φ’, is 

appropriate, and that the vertical (two-axis) tilt, φ , is less than 10

. The latter confirms the nadir and zenith

viewing angles (denoted υ hereafter) are correct. A complete experiment is made up of a set of 

approximately 10 microSAS measurement sequences, or casts. The casts are made at different mast index 

positions, m i, which were sequentially set going from 1 to 10 or 10 to 1 (in 1 m increments). 

103 
SIMBIOS Project Annual Report 

The most important aspects of r (865) as an analytical variable are as follows: 

1. It intrinsically includes the effects of changing solar illumination, because the sea-viewing 

observations are normalized by the sky radiance; and 

2. It can be used to create a severity index, in the sense that the stronger the artificial increase in LT 

(x, 865), the larger the increase in r (x, 865), and the magnitude of the departure from unity (or an 

appropriate reference value) is an estimate of the severity of the contamination. 

In Case-2 water conditions, or if the water type is close to the threshold of Case-1 and Case-2 

conditions, r (x, 865) is not expected to be unity even far from the tower, so the last point requires some

qualification: over the course of atower-perturbation experiment (usually requiring about 45 min), and in

the absence of a source of artificial reflections, r (x, 865) is expected to remain essentially constant. 

In other words, if the tower were not present, r (865) might not be unity, but it would remain constant over 

the time period of an experiment. 

Departures from constancy (i.e., above the level of environmental variability) as a function of x are 

expected to be an indicator of the presence of platform perturbations, particular if they show a significant 

increase as x decreaess. The constancy of r (865) can be quantified by selecting one of the most distant farfield observations within an experiment as a reference point, x. The RPD, ψ, is again used to quantify the 

dierence between an observation Y with respect to a reference value X. For the specific case of determining changes in r (865) as a function of x, the RPD (1) is computed as 

1

1

( ,865) ( ,865) ( ) 100

( ,865)

r x r x

x

r x

φ − = (8.4) 

where x

1 is the reference point in the calculation and ψ(x), the RPD value, is the so-called severity index. 

An ensemble description of the perturbation field, in terms of the severity index, can be created by

binning the RPD data as a function of x and averaging the values in each bin. Scaling the x values by the 

height of the sensor above the water (also approximating the height of the tower), H, permits a more 

generalized description of the data. The result of this process is shown in Fig. 8.3, along with delineations 

of the original minimum and maximum extents of the RPD values. The intersection of the perturbation 

curves with the scaled surface spot distance (x/H) gives the minimum, (expected) average, and maximum

severity indexes. The curves show three important aspects of the tower-perturbation field: 

1. The maximum perturbations occur very close to the tower (small values of x/H); 

2. As x/H increases and approaches 1 (i.e., as the surface spot becomes as far away as the platform is 

high), the platform perturbation curves converge towards smaller and smaller values; and 

3. For x>H, the platform perturbation is negligible. 

It is important to remember the perturbation here is modeled only in terms of the severity index. The 

azimuthal-viewing arcs in Fig. 3 (based on υ = 40◦) allow the RPD curves to be used to predict the severity 

index for a particular azimuthal-viewing angle. Conversely, the data in Fig. 8.3 can be used to determine 

what maximum azimuthal-viewing angle can be used while maintaining a certain severity index. For 

example, for a severity index of 3%, an instrument mounted on the edge of a platform (x

0 =0.0m) can be 

azimuthally rotated up to ± 23.10 (α is assumed symmetrical), and an instrument displaced 

3.3 m from the edge of the platform can be rotated up to ± 51.60

. 

A topic deserving additional consideration is the spectral aspects of the tower perturbation in the near 

field. It is important to remember that the severity index is only a relative diagnostic and cannot be used as 

an absolute predictor. The primary reason why a severity index versus spectral radiance approach will yield

a different description of the tower perturbation is the geometry at the time of sampling, both in terms of 

the sun and the mechanical pointing of the instruments. The geometry controls the importance of platform

shading versus superstructure reflections, and the resulting net perturbation from these contamination 

effects is strongly spectrally dependent. 

Alternative spectral analyses for the tower perturbation investigation based on above-water normalized 

water-leaving radiances are presented by Hooker and Zibordi (2003b). These analyses confirm the 

104 
SIMBIOS Project Annual Report 

generalized metric established with the original work by Hooker and Morel (2003): the primary avoidance 

principal for a sea-viewing sensor mounted on the illuminated side of a platform is that it must be pointed 

to a spot on the sea surface that is at least as far away as the platform is high. The successful application of 

the generalized sampling metrics established here to an automated above-water sampling system is presented by Zibordi et al. (2003c). 

It is important to remember this study was based on a simplistic and symmetric (box-like) structure. It

is likely that some of the results will not be immediately applicable to asymmetric or highly reflective 

platforms, and some extra considerations will have to be applied. For example, a sensor mounted on the 

bow of a ship, with the main superstructure set far back from the bow, will probably have to view a surface 

spot that is as far away as the height of the bow railing. Ships with superstructures set much closer to the 

bow, especially those painted white, will probably require a surface viewing distance that approximates the 

full height of the superstructure. 

In any case, each platform is a particular case, and each instrument mounting location a separate 

challenge. Although this study can provide guidance, it cannot answer all questions for all platforms. The 

most important lesson is that a perturbation analysis needs to be conducted for each above-water instrument 

location to determine the level of contamination as a function of the pointing parameters. For calibration 

and validation activities, sampling of the unperturbed far field will be a necessity that might not be easily 

satisfied without specially-designed mounting hardware. 

8.4 DATA PROCESSING PROTOCOLS 

Although above-water determinations of water-leaving radiances are part of the databases used to

create global bio-optical models, the majority of the data for these activities are from in-water 

measurements (O’Reilly et al. 2000). Part of this disparity is historical, in-water measurements have been 

conducted for a longer time period, and part of it is the consequence of the poor agreement that is

frequently obtained when the two methods are intercompared (Rhea and Davis 1997, Fougnie et al. 1999, 

Toole et al. 2000, and Hooker et al. 2002a), so traditional in-water measurements have been preferred. 

Aportion of the discrepancy between the two methods was recently shown to be caused by wave 

effects (Hooker et al. 2002a and Zibordi et al. 2002b), platform perturbations (Hooker and Morel 2003 and 

Hooker and Zibordi 2003b), and the anisotropy of the upwelled radiance field (Morel and Gentili 1996). 

The latter is particularly important, because in-water systems are usually nadir viewing, whereas abovewater systems are not). The study presented here builds on these accomplishments by analyzing 

simultaneous above-and in-water optical observations, wherein one of the two measurements was 

unequivocally free of platform perturbations, and implementing an above-water method with corrections 

for many problems unique to above-water methods. This data set is then used for the following objectives, 

which are examined within the generalized requirements of calibration and validation activities (i.e., the 

generalized 1% radiometry needed to satisfy the SeaWiFS absolute uncertainty requirement): a) evaluate 

the capabilities of above-water radiometry in shallow, coastal waters; and b) determine if the above-and inwater methods converge to within the uncertainties associated with the two methods. 

For a meaningful comparison with the (nadir-viewing) in-water sensors, the above-water methodology

needs to be corrected to account for the bidirectional dependency of the upward radiance field below the 

surface with that exiting the surface. The basic equations for this transformation (Morel and Gentili 1996 

and Mobley 1999) are an established part of the Protocols (Morel and Mueller 2002) and have been 

successfully incorporated into above-water measurements (Hooker and Morel 2003, Hooker and Zibordi

2003b, and Hooker et al. 2003c), so only only a brief summary is presented here. 

The radiance bidirectionality is parameterized by the so-called Q-factor, which takes a particular value, 

denoted Qn, for the nadir-viewing measurements. For above-water measurements, the angular parameters 

are imposed by the pointing angles of the sensors, as well as the surface effects of reflection and refraction. 

When dealing exclusively with Case-1 waters, the functional dependence of the variables can be simplified. 

In particular, it is assumed that the inherent optical properties are universally related to the chlorophyll a 

concentration (Morel and Prieur 1977), Ca. 

Because a nadir-transformed, above-water estimate of LW is equivalent to the in-water value, a 

formulation can be produced (Morel and Mueller 2002) to correct the S01 method, and is hereafter referred 

to as the Q02 method: 

105 
SIMBIOS Project Annual Report 

Fig. 8.3. The bin-averaged r (865) RPD values (solid circles and associated curve) as a function of x/H. 

Also shown are the curves delineating the minimum and maximum RPD values associated with the full 

data set (the x symbols). Superimposed on these curves are the azimuthal-viewing arcs based on υ =40◦ for 

a sensor mounted on the edge of a platform (x

0 =0.0m) and one mounted at mi =1 (the first mast index 

position) on the HDS (x

0 =3.3 m). Each viewing arc gives the scaled distance to the surface spot (x/H) asa 

function of α. The x0 =3.3m arc ends at x

0 /H =0.25 (α =90◦), although, lower x/H values could be 

measured. The inset panel shows a schematic of the localized (x,y) coordinate system, along with the 

geometry for the pointing angles with respect to the sun (γ) and the surface spot viewed by the sea-viewing 

sensor (α). Note the origin of the localized coordinate system (denoted by the intersection of the +x and +y 

axes) is a point at the northwest corner of the tower within the area associated with the flying bridge. 

1

02 0

1

( , , , ) ˆ ( ) 1( ) ( , ) ( , , )

Q a

W

n a

Q C L

W Q C

λ θ φ λ Lˆ λ

θ λ θ

R = R (8.5) 

where the Q terms are evaluated at null depth (z =0-

), θ is the solar zenith angle, θ’ is the above-water 

viewing angle (υ) refracted by the air–sea interface, and the factor merges all the effects of reflection and 

106 
SIMBIOS Project Annual Report 

refraction (the 0 term is evaluated at nadir, i.e., θ = 0). All the correction terms are computed here from

look-up tables (Morel et al. 2002). An above-water instrument system cannot be floated away from a 

sampling platform, but this is easily and effectively accomplished with many in-water profiler designs. 

Within the context of this generalized difference between the two methods, an above-water acquisition 

effort—even if platform perturbations are recognized and minimized—is still more likely to contain 

platform-contaminated acquisition sequences than profiles from a simultaneous in-water method. There are 

exceptions to this generality, but it remains more true than not, and is one of the reasons why in-water 

calibration and validation exercises predominate. Using these arguments as an overall rationale for 

prioritizing the data used in this study, the analytical perspective adopted here is as follows: 

1. Use the in-water observations as the reference measurement for evaluation purposes. 

2. When matching the above-and in-water observations, use only simultaneous or nearly

simultaneous (within ± 5 min) data acquisition sequences to minimize environmental influence. 

3. Use radiometers with absolute calibrations from separate calibration facilities (the usual case for 

most investigators), but intercalibrate the sensors to quantify the effect of eliminating differences 

in the calibration standards and procedures. 

Although the discussion contained within the aforementioned rationale readily supports a decision to

use the in-water measurements as the analytical reference, another reason for doing so is the in-water data

processor used here has been evaluated in a data processor round robin and its uncertainties are well

quantified (Hooker et al. 2001). 

An intercomparison of the intercalibrated above-and in-water methods, for all the Case-1 and Case-2 

data (Loisel and Morel 1998) is presented in Fig. 8.4. With the exception of a small number of values at

555 nm, the majority of the data are well distributed around the 1:1 line, although the lower edge of the 

variance is defined by a large number of the values at 510 nm. The AAOT above-water measurements were 

unequivocally free of platform perturbations, whereas for the ADRIA-2000 data, the in-water observations 

were free of perturbations. Because the Fig. 8.4 data do not show any significant biases, platform perturbations were properly avoided, and the Case-2 data are not substantially different from the Case-1 data. The 

average RPD of all the intercalibrated data is approximately 1.8%, and if the independently calibrated data 

are considered, the average RPD increases to about 2.3%. In either case, the average RPD values are to

within the uncertainties associated with the absolute calibration of the radiometers (Hooker et al. 2002b). 

Additional details on the total uncertainty budget of both methods are presented by Hooker et al. (2003c). 

An interesting aspect of the Fig. 8.4 results is the very good agreement for the overcast data (much of 

the data at approximately 0.3 μWcm−2 nm−2 sr−1

 

and below are from overcast stations). This is an added 

capabilitiy of the above-water method used here, and shows an enhanced flexibility of the method. It can be 

particularly important in some circumstances, because cloud conditions are not predetermined by any 

means, and many field expeditions get only limited opportunities to sample certain regimes. 

The importance of properly accounting for bidirectional effects and accurately determining the surface 

reflectance is demonstrated by replacing (λ) with 02 ˆ ( ) QLW λ with 95 ˆ ( ) S LW λ in the intercomparisons of the 

intercalibrated results (the S95 method assumes ρ =0.028 and there is no Q-factor correction). The results 

of this substitution is a significant bias—almost all the data are above the 1:1 line—that is, the S95 method 

overestimates the water-leaving radiances across all bands. The average magnitude of the overestimation is

approximately 6.6%, which is about 4.8% above the Q02 intercalibrated results. This level of difference is

in keeping with the differences in above-and in-water methods documented by Hooker et al. (2002a), 

wherein the former were not bidirectionally corrected. 

The SeaWiFS uncertainty requirements were based primarily on an open ocean perspective. The data 

presented here were collected in the coastal environment, so the level of agreement that has been achieved 

is significantly better than originally anticipated. The convergence of the Q02 above-water method with the 

traditional in-water technique was primarily the result of careful metrology, platform perturbation 

avoidance, state-of-the-art corrections to the primary variables, plus comprehensive and independent

evaluations of the calibration and data processing schemes. If this level of effort is reproduced in similar or

simpler environments (coastal waters predominated by Case-1 conditions or the open ocean), there is every 

reason to believe that the Q02 above-water method can be used at the same level of effcacy as an in-water 

107 
SIMBIOS Project Annual Report 

method. Furthermore, this should be equally true both for calibration and validation activities and biooptical modeling requirements. 

Fig. 8.4. An intercomparison of intercalibrated (Q-corrected) above-water determinations of water-leaving 

radiances, 02 ˆ ( ) QLW λ , with simultaneous in-water determinations, ( ) L

W λ , for Case-1 and Case-2 

conditions. The data were collected at the AAOT under sampling geometries that avoided all platform

perturbations and during the Adriatic Data collection for Research on marine Inherent and Apparent optical

properties field campaign (conducted in 2000 and referred to hereafter as ADRIA-2000). The majority of 

the data are for Case-1 conditions (about 85%), and the Case-2 stations were close to the threshold between 

the two water typesas defined by Loisel and Morel (1998). The vast majority of all optical data were 

collected under clear-sky conditions, and the average environmental conditions were excellent: cloud cover 

was less than 2/10, wind speed was less than 5 m s

-1

, and wave height was 0.3 m or less. Water depth at the 

AAOT is 17 m, wheras it ranged from 7.4–32.3 m during ADRIA-2000. 

108 
SIMBIOS Project Annual Report 

8.6 DATA ARCHIVE 

The SIRREX-8 activity established that the immersion factors originally supplied with the SeaWiFS 

Field Team profiling equipment were significantly incorrect (by more than 10% in the blue part of the 

spectrum). Consequently, the entire archive of in situ optical measurements are being reprocessed. This 

activity has been coordinated with the production of a new optical data processor, so many of the data

processing protocols that were refined during the investigations for the SIMBIOS project could be utilized. 

The reprocessing is in the final stages of quality assurance, and a preliminary assessment indicates a 

substantial improvement in many of the primary and bio-optical variables. A final set of water-leaving

radiances and ancillary parameters for those campaigns that were not specialized experiments (like the 

tower perturbation investigations) will be delivered to the SeaWiFS Bio-Optical Archive and Storage 

System (SeaBASS) before the end of 2003. 

REFERENCES 

Aas, E., 1969: On Submarine Irradiance Measurements. University of Copenhagen Report No. 6, 

University of Copenhagen Institute for Physical Oceanography, Copenhagen, Denmark, 23 pp plus 

tables and figures. 

Atkins, W.R.G., and H.H. Poole, 1933: The photo-electric measurement of the penetration of light of 

various wave lengths into the sea and the physiological bearing of results. Phil. Trans. Roy. Soc. 

London, 222, 129–164. 

Berger, F., 1958: Uber die ursache des “oberflacheneffekts” bei lichtmessungen unter wasser. Wetter ü.

Leben, 10, 164– 170 (translated from German). 

Berger, F., 1961: Uber den “Taucheffekt” bei der lichtmessunguber und unter wasser. Arch. Meteorol. 

Wien., 11, 224–240 (translated from German). 

Berthon, J-F., G. Zibordi, J.P. Doyle, S. Grossi, D. Van der Linde, and C. Targa, 2002: Coastal Atmosphere 

and Sea Time Series (CoASTS), Part 2: Data Analysis. NASA Tech. Memo. 2002–206892, Vol. 20, 

S.B. Hooker and E.R. Firestone, Eds., NASA Goddard Space Flight Center, Greenbelt, Maryland, 25 

pp. 

Claustre, H., S.B. Hooker, L. Van Heukelem, J-F. Berthon, R. Barlow, J. Ras, H. Sessions, C. Targa, C.

Thomas, D. van der Linde, and J-C. Marty, 2003: An intercomparison of HPLC phytoplankton 

pigment methods using in situ samples: Application to remote sensing and database activities. Marine 

Chem., (accepted). 

Fougnie, B., R. Frouin, P. Lecomte, and P-Y. Deschamp, 1999: Reduction of skylight reflection effects in 

the above-water measurement of diffuse marine reflectance. Appl. Opt., 38, 3,844–3,856. 

Hooker, S.B., and W.E. Esaias, 1993: An overview of the Sea-WiFS project. Eos, Trans., Amer. Geophys. 

Union, 74, 241–246. , 

Hooker, S.B., and C.R. McClain, 2000: The Calibration and Validation of SeaWiFS Data. Prog. 

Oceanogr., 45, 427–465. 

Hooker, S.B., G. Zibordi, J-F. Berthon, D. D’Alimonte, S. Maritorena, S. McLean, and J. Sildam, 2001:

Results of the Second Sea-WiFS Data Analysis Round Robin, March 2000 (DARR-00). NASA Tech. 

Memo. 2001–206892, Vol. 15, S.B. Hooker and E.R. Firestone, Eds., NASA Goddard Space Flight 

Center, Greenbelt, Maryland, 71 pp. 

109 
SIMBIOS Project Annual Report 

Hooker, S.B., G. Lazin, G. Zibordi, and S. McLean. 2002a. An evaluation of above-and in-water methods 

for determining water-leaving radiances. J. Atmos. Oceanic Technol., 19, 486– 515. 

Hooker, S.B., S. McLean, J. Sherman, M. Small, G. Lazin, G. Zibordi, and J.W. Brown, 2002b: The 

Seventh SeaWiFS Inter-calibration Round-Robin Experiment (SIRREX-7), March 1999. NASA Tech.

Memo. 2002–206892, Vol. 17, S.B. Hooker and E.R. Firestone, Eds., NASA Goddard Space Flight 

Center, Greenbelt, Maryland, 69 pp. 

Hooker, S.B.,, and A. Morel, 2003: Platform and environmental effects on above-and in-water 

determinations of water-leaving radiances. J. Atmos. Oceanic Technol., 20, 187–205. 

Hooker, S.B., and G. Zibordi, 2003a: Advanced methods for characterizing the immersion factor of marine 

radiometers. Appl. Opt., (submitted). 

Hooker, S.B., and , 2003b: Platform perturbations in above-water radiometry. Appl. Opt., (submitted). 

Hooker, et al., 2003a: The Second SeaWiFS HPLC Analysis Round-Robin Experiment (SeaHARRE-2). 

NASA Tech. Memo. 2003, NASA Goddard Space Flight Center, Greenbelt, Maryland, (in prep.). 

Hooker, S.B., G. Zibordi, J-F. Berthon, D. D’Alimonte, D. van der Linde, and J.W. Brown, 2003b: TowerPerturbation Measurements in Above-Water Radiometry. NASA Tech. Memo. 2003–206892, Vol. 23, 

S.B. Hooker and E.R. Fire-stone, Eds., NASA Goddard Space Flight Center, Greenbelt, Maryland, 35 

pp. 

Hooker, S.B., and G. Zibordi, J-F. Berthon, and J.W. Brown, 2003c: Above-water radiometry in shallow, 

coastal waters. Appl. Opt., (submitted). 

Joint Global Ocean Flux Study, 1991: JGOFS Core Measurements Protocols. JGOFS Report, No. 6,

Scientific Committee on Oceanic Research, 40 pp. 

Loisel, H., and A. Morel, 1998: Light scattering and chlorophyll concentration in case 1 waters: A 

reexamination. Limnol. Oceanogr., 43, 847–858. 

Mobley, C.D., 1999: Estimation of the remote-sensing reflectance from above-surface measurements. Appl. 

Opt., 38, 7,442– 7,455. 

Morel, A., 1980: In-water and remote measurements of ocean color. Bound.-Layer Meteorol., 18, 177–201. 

Morel, A., and L. Prieur, 1977: Analysis of variations in ocean color. Limnol. Oceanogr., 22, 709–722. 

Morel, A., and B. Gentili, 1996: Diffuse reflectance of oceanic waters, III. Implication of bidirectionality 

for the remote sensing problem. Appl. Opt., 35, 4,850–4,862. 

Morel, A., and J.L. Mueller, 2002: “Normalized Water-Leaving Radiance and Remote Sensing Reflectance: 

Bidirectional Reflectance and Other Factors.” In: J.L. Mueller and G.S. Fargion, Ocean Optics 

Protocols for Satellite Ocean Color Sensor Validation, Revision 3, Volume 2. NASA Tech. Memo. 

2002–210004/Rev3–Vol2, NASA Goddard Space Flight Center, Greenbelt, Maryland, 183–210. 

Morel, A., D.Antoine, and B. Gentilli, 2002: Bidirectional reflectance of oceanic waters: Accounting for 

Raman emission and varying particle scattering phase function. Appl. Opt., 41, 6,289–6,306. 

Mueller, J.L., 2000: “Overview of Measurement and Data Analysis Protocols” In: G.S. Fargion and J.L. 

Mueller, Ocean Optics Protocols for Satellite Ocean Color Sensor Validation, Revision 2. NASA Tech. 

Memo. 2000–209966, NASA Goddard Space Flight Center, Greenbelt, Maryland, 87– 97. 

110 
SIMBIOS Project Annual Report 

Mueller, J.L., 2002: “Overview of Measurement and Data Analysis Protocols.” In: J.L. Mueller and G.S. 

Fargion, Ocean Optics Protocols for Satellite Ocean Color Sensor Validation, Revision 3, Volume 1. 

NASA Tech. Memo. 2002– 210004/Rev3–Vol1, NASA Goddard Space Flight Center, Greenbelt, 

Maryland, 123–137. 

Mueller, J.L., 2003: “Overview of Measurement and Data Analysis Methods.” In: Mueller, J.L., and et al., 

Ocean Optics Protocols for Satellite Ocean Color Sensor Validation, Revision 4, Volume III: 

Radiometric Measurements and Data Analysis Protocols. NASA Tech. Memo. 2003– 211621/Rev4–

Vol.III, NASA Goddard Space Flight Center, Greenbelt, Maryland, 1–20. 

Mueller, J.L., and R.W. Austin, 1992: Ocean Optics Protocols for SeaWiFS Validation. NASA Tech. Memo. 

104566, Vol. 5, S.B. Hooker and E.R. Firestone, Eds., NASA Goddard Space Flight Center, Greenbelt, 

Maryland, 43 pp. 

Mueller, J.L. and R.W. Austin, 1995: Ocean Optics Protocols for Sea- WiFS Validation, Revision 1. NASA

Tech. Memo. 104566, Vol. 25, S.B. Hooker, E.R. Firestone, and J.G. Acker, Eds., Vol. 25, S.B.

Hooker, E.R. Firestone, NASA Goddard Space Flight Center, Greenbelt, Maryand J.G. Acker, Eds., 

land, 66 pp. 

Mueller, J.L., F. M«elin, S.B. Hooker, D. D’Alimonte, and B. Holben, 2003c: An autonomous above-water 

system for the vali dation of ocean color radiance data. Trans. IEEE Trans. Geosci. Remote Sensing., 

(accepted). 

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

D.A. Siegel, K.L. 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. Geo phys. Res., 

103, 24,937–24,953. 

O’Reilly, and 24 Coauthors, 2000: SeaWiFS Postlaunch Calibration and Validation Analyses, Part 3. NASA

Tech. Memo. 2000–206892, Vol. 11, S.B. Hooker and E.R. Firestone, Eds., NASA Goddard Space 

Flight Center, 49 pp. 

Petzold, T.J., and R.W. Austin, 1988: Characterization of MER 1032. Tech. Memo. EN–001–88t, Vis. Lab., 

Scripps Institution of Oceanography, La Jolla, California, 56 pp. plus appendices. 

Rhea, W.J., and C.O. Davis, 1997: A coparison of the SeaWiFS chlorophyll and CZCS pigment algorithms

using optical data from the 1992 JGOFS Equatorial Pacific Time Series. Deep Sea Res. II, 44, 1,907–

1,925. 

Smith, R.C., 1969: An underwater spectral irradiance collector. J. Mar. Res., 27, 341–351. 

Toole, D.A., D.A. Siegel, D.W. Menzies, M.J. Neumann, and R.C. Smith, 2000: Remote sensing 

reflectance determinations in the coastal ocean environment—impact of instrumental characteristics 

and environmental variability. Appl. Opt., 39, 456–469. 

Van der Linde, D., 2003: The AAOT Deployment Systems: An Overview. EUR Report 20548 EN, Joint 

Research Centre, Ispra, Italy, 13 pp. 

Voss, K.J., J.W. Nolten, and G.D. Edwards, 1986: Ship shadow effects on apparent optical properties. 

Proc. Soc. Photo Opt., Instrum. Eng., Ocean Optics XII, 2,258, 815–821. 

Westlake, D.F., 1965: Some problems in the measurement of radiation under water: A review. Photochem. 

Photobiol., 4, 849–868. 

111 
SIMBIOS Project Annual Report 

Zibordi, G., J.P. Doyle, and S.B. Hooker, 1999: Offshore tower shading effects on in-water optical

measurements. J. At mos. Ocean. Tech., 16, 1,767–1,779. 

Zibordi, G., J-F. Berthon, J.P. Doyle, S. Grossi, D. van der Linde, C. Targa, and L. Alberotanza, 2002a:

Coastal Atmosphere and Sea Time Series (CoASTS), Part 1: A Tower-Based Long-Term

Measurement Program. NASA Tech. Memo. 2002–206892, Vol. 19, S.B. Hooker and E.R. Firestone, 

Eds., NASA Goddard Space Flight Center, Greenbelt, Maryland, 29 pp. 

Zibordi, G., S.B. Hooker, J-F. Berthon, and D. D’Alimonte, 2002b: Autonomous above-water radiance 

measurements from an offshore platform: A field assessment experiment. J. At mos. Oceanic Technol., 

19, 808–819. 

Zibordi, G., S.B. Hooker, J. Mueller, G. Lazin, 2003a: Characterization of the immersion factor for a series 

of underwater opti cal radiometers. J. Atmos. Oceanic Technol., (accepted). 

Zibordi, G.,D. D’Alimonte, D. van der Linde, S.B. Hooker, J.W. Brown, 2003b: New Laboratory Methods 

for Characterizing the Immersion Factors of Irradiance Sensors. NASA Tech. Memo. 2003–206892, 

Vol. 26, S.B. Hooker and E.R. Firestone, Eds., NASA Goddard Space Flight Center, Greenbelt, 

Maryland, 34 pp. 

This Research was Supported by 

the NASA SIMBIOS Program 

Hooker, S.B., and A. Morel, 2003: Platform and environmental effects on above-and in-water 

determinations of water-leaving radiances. J. Atmos. Oceanic Technol., 20, 187– 205. 

Johnson, B.C., S.W. Brown, K.R. Lykke, C.E. Gibson, G. Fargion, G. Meister, S.B. Hooker, Brian 

Markham, and J.J. Butler, 2003: Comparison of cryogenic radiometry and thermal radiometry

calibrations at NIST using multichannel filter radiometers. Metrologia, 40, S216–S219. 

Claustre, H., S.B. Hooker, L. Van Heukelem, J-F. Berthon, R. Barlow, J. Ras, H. Sessions, C. Targa, C.

Thomas, D. van der Linde, and J-C. Marty, 2003: An intercomparison of HPLC phytoplankton 

pigment methods using in situ samples: Application to remote sensing and database activities. Marine 

Chem., (accepted). 

Zibordi, G., S.B. Hooker, J. Mueller, G. Lazin, 2003: Characterization of the immersion factor for a series 

of underwater optical radiometers. J. Atmos. Oceanic Technol., (accepted). 

McClain, C.R., G.C. Feldman, S.B. Hooker, 2003: An overview of the SeaWiFS project and strategies for 

producing a climate research quality global ocean bio-optical time series. Deep Sea Res., (submitted). 

Zibordi, G., F. Mélin, S.B. Hooker, D. D’Alimonte, and B. Holben, 2003: An autonomous above-water 

system for the validation of ocean color radiance data. Trans. IEEE Trans. Geosci. Remote Sensing.,

(accepted). 

Hooker, S.B., and G. Zibordi, 2003: Platform perturbations in above-water radiometry. Appl. Opt.,

(submitted). 

Hooker, S.B., and G. Zibordi, J-F. Berthon, and J.W. Brown, 2003: Above-water radiometry in shallow, 

coastal waters. Appl. Opt., (submitted). 

Hooker, S.B., and G. Zibordi, 2003: Advanced methods for characterizing the immersion factor of marine 

radiometers. Appl. Opt., (submitted). 

112 
SIMBIOS Project Annual Report 

Hooker, S.B., G. Zibordi, J-F. Berthon, D. D’Alimonte, D. van der Linde, and J.W. Brown, 2003: TowerPerturbation Measurements in Above-Water Radiometry. NASA Tech. Memo. 2003–206892, Vol. 23,

S.B. Hooker and E.R. Fire-stone, Eds., NASA Goddard Space Flight Center, Greenbelt, Maryland, 35 

pp. 

Doyle, J., G. Zibordi, J-F. Berthon, D. van der Linde, and S.B. Hooker, 2003: Validation of an In-Water, 

Tower-Shading Correction Scheme. NASA Tech. Memo. 2003–206892, Vol. 25, S.B. Hooker and E.R.

Firestone, Eds., NASA Goddard Space Flight Center, Greenbelt, Maryland, 32 pp. 

Zibordi, G., D. D’Alimonte, D. van der Linde, S.B. Hooker, J.W. Brown, 2003: New Laboratory Methods 

for Characterizing the Immersion Factors of Irradiance Sensors. NASA Tech. Memo. 2003–206892, 

Vol. 26, S.B. Hooker and E.R. Firestone, Eds., NASA Goddard Space Flight Center, Greenbelt, 

Maryland, 34 pp. 

Barlow, R., H. Sessions, N. Silulwane, H. Engel, S.B. Hooker, J. Aiken, J. Fishwick, V. Vicente, A. Morel,

M. Chami, J. Ras, S. Bernard, M. Pfaff, J.W. Brown, and A. Fawcett, 2003: BENCAL Cruise Report.

NASA Tech. Memo. 2003– 206892, Vol. 27, S.B. Hooker and E.R. Firestone, Eds., NASA Goddard 

Space Flight Center, Greenbelt, Maryland, 64 pp. 

Hooker, S.B., L. Van Heukelem, C.S. Thomas, H. Claustre, J. Ras, L. Schl¬uter, J. Perl, C. Trees, V. 

Stuart, E. Head, R. Barlow, H. Sessions, L. Clementson, J. Fishwick, and J. Aiken, 2003: The Second 

SeaWiFS HPLC Analysis Round-Robin Experiment (SeaHARRE-2). NASA Tech. Memo. 2003, NASA 

Goddard Space Flight Center, Greenbelt, Maryland, (in prep.). 

113 
SIMBIOS Project Annual Report

Chapter 9

Optimization Of Ocean Color Algorithms: Application 

To Satellite And In Situ Data Merging. 

Stéphane Maritorena and David A. Siegel 

Institute for Computational Earth System Science, UCSB, Santa Barbara, CA 93106-

3060, USA 

André Morel 

Laboratoire de Physique et Chimie Marines, Villefranche Sur Mer, France 

9.1 INTRODUCTION 

The objective of our program is to develop and validate a procedure for ocean color data merging 

which is one of the major goals of the SIMBIOS project (McClain et al., 1995). The need for a merging 

capability is dictated by the fact that since the launch of MODIS on the Terra platform and over the next

decade, several global ocean color missions from various space agencies are or will be operational 

simultaneously. The apparent redundancy in simultaneous ocean color missions can actually be exploited to 

various benefits. The most obvious benefit is improved coverage (Gregg et al., 1998; Gregg & Woodward, 

1998). The patchy and uneven daily coverage from any single sensor can be improved by using a 

combination of sensors. Beside improved coverage of the global ocean the merging of ocean color data 

should also result in new, improved, more diverse and better data products with lower uncertainties. 

Ultimately, ocean color data merging should result in the development of a unified, scientific quality, ocean 

color time series, from SeaWiFS to NPOESS and beyond. 

Various approaches can be used for ocean color data merging and several have been tested within the 

frame of the SIMBIOS program (see e.g. Kwiatkowska & Fargion, 2003, Franz et al., 2003). As part of the 

SIMBIOS Program, we have developed a merging method for ocean color data. Conversely to other 

methods our approach does not combine end-products like the subsurface chlorophyll concentration (chl) 

from different sensors to generate a unified product. Instead, our procedure uses the normalized waterleaving radiances (LwN(λ)) from single or multiple sensors and uses them in the inversion of a semianalytical ocean color model that allows the retrieval of several ocean color variables simultaneously. 

Beside ensuring simultaneity and consistency of the retrievals (all products are derived from a single 

algorithm), this model-based approach has various benefits over techniques that blend end-products (e.g. 

chlorophyll): 1) it works with single or multiple data sources regardless of their specific bands, 2) it 

exploits band redundancies and band differences, 3) it accounts for uncertainties in the LwN(λ) data and, 4) 

it provides uncertainty estimates for the retrieved variables. 

9.2 RESEARCH ACTIVITIES 

Development of an ocean color database and algorithm development 

Over the past 3 years, we have assembled a large comprehensive in situ ocean color data set that 

contains inherent (IOP) and apparent (AOP) optical properties as well as chlorophyll a concentration data

from various locations. This database is designed for ocean color algorithm development and is well suited 

for semi-analytical algorithm development in particular. Since it contains both IOPs and AOPs, this

database is better suited for semi-analytical algorithms development than data sets like the one used during 

SeaBAM (O'Reilly et al., 1998) which only contained chl and remote sensing reflectance data. Most of the 

data included in the database come from the NASA SIMBIOS SeaBASS archive but several investigators 

114 
SIMBIOS Project Annual Report

have provided data sets or subsets directly to us. Various quality control (QC) procedures have been

developed (Fargion & McClain, 2003) to identify corrupted data, outliers or specific bio-optical situations. 

The database contains chlorophyll a concentration, diffuse attenuation coefficients, LwN, particulate 

backscattering and component absorption (phytoplankton, detrital, dissolved). Most of the absorption data

are hyperspectral. The current status of the IOP/AOP data set is described in Table 9.1. 

Table 9.1: Status of the AOP/IOP data set. The first number in each cell indicates the number of stations for 

which data are available. The numbers in parentheses indicate the number of available wavelengths 

EXPERIMENT Chl a Kd Rrs bb ad ag ap

AAOT 94 - 35 (5) - - - -

Aerosols Indoex 53 3367 (12) 5309 (6) - 70 (225) 70 (200) 73 (225)

Ace-Asia 116 696 (18) 903 (18) 780 (6) 48 (225) 69 (200) 48 (225)

AMLR 268 1060 (18) 1302 (18) - 101 (225) 91 (200) 101 (225)

Atlantic Meridional Transect 281 281 (5) 281 (5) - - - -

Bermuda Bio-Optics Project 406 4024 (16) 6505 (12) - 77 (520) 77 (520) 77 (520)

Arc00 159 - 7262 (5) - - - -

BIOCOMPLEXITY 300 - 6505 (13) - 74 (500) 74 (315) 74 (500)

CALCOFI 430 938 (12) 1064 (23) - 159 (225) 130 (200) 143 (225)

CaTS 11 - 16 (5) - - - -

CoJet 53 - 74 (15) - - - -

CSC 23 - 23 (5) - - - -

DOER 10 - 10 (5) - - - -

ECOFRONT 37 3142 (6) 2168 (7) - 16 (225) 16 (200) 16 (225)

Ecology of Harmful Algal Blooms 429 - 246 (200) - 422 (200) 398 (200) 422 (200)

EPIC 152 1988 (5) 3179 (5) - - - -

JGOFS-EQPAC 125 1785- 1737 (5) - - - -

Globec Biomapper 45 - - - 43 (200) 43 (200) 43 (200)

GOCAL NA - 1328 (7) 28 (6) - - -

NASA-Gulf of Maine 148 - - - 150 (200) 137 (200) 150 (200)

NOAA-Gulf of Maine 316 - - - - - -

JGOFS-SIO 89 1671 (12) 1875 (12) - 23 (225) 23 (225) 23 (225)

JGOFS-Arabian Sea 2693 3085 (5) 3083 (5) - - - -

Kieber Photochemistry 2002 427 1791 (5) 1772 (5) - - - -

Lab2000 51 - 3191 (5) - - - -

Lab1997 204 - - - - - -

Lab1996 42 - 113 (12) - 122 (500) 74 (500) 122 (500)

LMER-TIES 237 - 76 (7) - 85 (250) 85 (200) 85 (250)

MBARI 34 - 34 (5) - - - -

MOCE 13 - 13 (5) - - - -

North Atlantic Bloom Experiment 110 609 (5) 431 (5) - - - -

Ocean Research Consortium 17 - 18 (10) 14 (6) 16 (470) 16 (470) 16 (470)

Plumes and Blooms 786 7702 (7) 10330 (12) 3867 (7) 500 (13) 431 (13) 505 (13)

ROAVERRS 162 - 162 (5) - - - -

Sea of Japan 54 593 (12) 777 (12) 666 (6) 51 (225) 51 (200) 51 (225)

Scotia Prince Ferry 7420 - 60 (5) 7147 (3) - 6591 (9) 6478 (9)

Tongue of the Ocean 88 - 86 (200) 16 (6) 95 (200) 95 (225) 95 (200)

World Ocean Circulation Experime 68 - 68 (5) - - - -

Total Measurements 15951 32732 60036 12518 2052 8471 8522

The IOP/AOP database is designed in part to help the development of our merging model. The 

transformation of the model into a fully hyperspectral mode (which would make it usable right away with

any sensor) and improved parameterizations of some of the components of the model are the major 

modifications we are trying to implement. A preliminary version of the hyperspectral model has been 

developed and optimized using data from the AOP/IOP database. Although it shows some good overall

results for all three retrieved variables this preliminary hyperspectral version does not always perform well 

115 
SIMBIOS Project Annual Report

at the extremes of the chlorophyll range (either in very clear or very rich waters). This new version of the 

model still requires some work and a more conservative, step-by-step approach is now used for its 

development.

Satellite Ocean Color Data Merging

Our approach for ocean color data merging is based on the inversion of a semi-analytical model that 

relates LwN to the backscattering and absorption coefficients (Eq. 9.1) as described in Gordon et al. (1988). 

 b ( ) + b ( ) + a ( ) + a ( ) + a ( )

b ( ) + b ( )

g

t F ( ) L ( ) =

i

2

i =1 bw bp w ph cdm

bw bp

2 i

0

wN ∑ 















λ λ λ λ λ

λ λ λ

λ

w n (9.1) 

Each of the non-water components in a and bb is expressed as a known shape function with an unknown 

magnitude:

aph(λ) = Chl aph*(λ) (9.2) 

acdm(λ) = acdm(443) exp(-S(λ-443) (9.3) 

bbp(λ) = bbp(443) (λ/443)-η (9.4) 

where t, nw, gi, Fo(λ), aw(λ) and bbw(λ) at taken from the literature whereas η, S, aph*(λ) were determined by

“tuning” the model against a large in situ data set (Maritorena et al., 2002). A non-linear least-square fitting 

technique is used to solve for the unknowns (chl, acdm(443) and bbp(443)) from LwN(λ) data at 4 or more 

wavelengths. The model also provides uncertainty estimates for each of the retrievals using a linear 

approximation to the calculation of non-linear regression inference regions (Bates & Watts, 1988). The 

model, hereafter referred to as the GSM01 model, is fully described in Garver & Siegel (1997) and 

Maritorena et al. (2002). 

Since the model retrievals are generated using a curve-fitting technique that minimizes the leastsquares differences between the measured and modeled LwN(λ), it is straightforward to use the GSM01

model with multiple data sets like in data merging. When multiple data sets are used (e.g. two or more 

sensors have LwN measurements over a given pixel), the LwN(λ) data from all available sources are 

concatenated (as are the relevant wavebands information) allowing the curve-fitting step to be conducted 

with more data points than with a single source. When data sources have different bands, the fitting

procedure also benefits from an increased spectral resolution. A key aspect of the procedure is that there is 

no transformation or averaging of the input LwN data, they are used "as is" in the curve-fitting technique. A 

schematic of the input and output products of the merging model is presented in figure 9.1. 

Our model-based approach for ocean color data merging was first tested using SeaWiFS and MOS data 

and results were presented during the SIMBIOS science Team meeting in Baltimore (Jan. 15-17, 2002). 

Since then, we have successfully used SeaWiFS and MODIS data (from both the Terra and Aqua 

platforms). We have tested our merging approach using daily level-3 data from SeaWiFS (reprocessing # 4, 

9 km) and MODIS (collection # 4, 4.6 km) for 18 dates between December 4, 2000 and March 22, 2003. 

Only the MODIS “best quality” data (i.e. quality 0) were used during these tests. Since the SeaWiFS and

MODIS LwN(λ) data products have different spatial resolution, it is necessary to first adapt the MODIS data 

to the SeaWiFS resolution by averaging four 4.6 km bins into a 9 km bin and to have the 2 data sources set

to a common binned grid. The data are processed between 65 degrees North and 65 degrees South. We

have focused most of our effort on the period for which MODIS Terra collection # 4 products are validated 

(11/1/2000-3/19/2002). Some data outside this time window were also used to illustrate 

SeaWiFS/Terra/Aqua data merging and to assess improvement in coverage when merging 3 different ocean 

color data sources. 

116 
SIMBIOS Project Annual Report

9.3 RESEARCH RESULTS 

The aim of our SIMBIOS work is to demonstrate the feasibility of an ocean color data merging 

procedure based on a semi-analytical mode that uses LwN(λ) data from one or more sources. It is out of the 

scope of this report to document the accuracy of the model retrievals with in situ or satellite data (but see 

Maritorena et al., 2002; Siegel et al., 2002 and Siegel et al., 2003. Examples of global maps of chlorophyll

a concentration, acdm(443) and bbp(443) generated by the GSM01 merging model with SeaWiFS and 

MODIS-Terra LwN(λ) data are presented in figure 9.2. Global maps of chl, acdm(443) and the bbp(443) 

images generated by the GSM01 merging model using the Terra and SeaWiFS data show very good 

consistency overall (Maritorena et al., 2003). In general, the retrieved fields do not show discontinuities 

when the model switched from an area with a single data source to an area where both SeaWiFS and 

MODIS LwN(λ) data were used. This reflects the generally good agreement between the LwN(λ) data from

both sources and the robustness of the model. The level of agreement between the two sensors also has an 

influence on the estimated uncertainties of the derived products. When considering the pixels that are 

covered by both sensors, a very large majority of them show reduced uncertainties in the merged products

compared to those generated from a single data source. Figure 9.3 shows the frequency distribution of the 

ratio of the Chl uncertainties using either SeaWIFS or MODIS LwN data alone over the Chl uncertainties 

when both sources are used. Overall, the uncertainties tend to decrease when multiple sources are used and

this is true for all 3 products generated by the GSM01 model. This decrease in the uncertainties of the 

derived products is observed for all products and at all the dates we have processed. In the worst case, 70% 

of the pixels showed lower uncertainties in the merged products. Uncertainties are generally higher in the 

merged products when LwN data do not agree well between the data sources. 

Figure 9.1: Schematic of the input and output data of the semi-analytical ocean color merging model. 

This is illustrated in figure 9.4 where LwN(443) from both sensors are compared when uncertainties 

improved in the merged product (merged/unmerged uncertainty ratio of 0.5 or less) and when they got

worse (uncertainty ratio > 1). When the uncertainties are lower in the merged product, the agreement

between both sets of LwN(λ) is generally very good with most of the data points on or very close to the 1:1 

line (figure 9.4 shows the 443 data but this is true for all bands). When the uncertainties were higher in the 

117 
SIMBIOS Project Annual Report

merged images, the LwN(λ) data showed some clear differences between SeaWiFS and MODIS. Areas 

where the products uncertainties are actually higher in the merged image are frequently next to gaps caused 

by sun glint, gaps between the swaths or clouds. They also appear to be mostly located in the south 

hemisphere. Further analyses are needed to assess what causes these features. 

Improvement in coverage is obvious. The daily surface area effectively covered by any individual 

sensor depends upon various factors such as the sensor’s technical and orbital characteristics, sun glint,

cloud cover and season. The increased coverage that results from the use of multiple data sources is 

illustrated in figure 9.5 for the 18 dates we have used. Daily coverage jumps from ~12-15% of the ocean 

surface (in the 65N to 65S range) when SeaWiFS is used alone to ~25% when it is used with MODISTerra. When MODIS-Aqua data are used in the merging process along with SeaWiFS and Terra the daily

percentage coverage reaches ~30-35% to the ocean surface. These numbers agree well with those derived 

from a theoretical analysis prior to SeaWiFS and MODIS launches (Gregg and Woodward, 1998) as well as 

with those obtained by the SIMBIOS Project with their Level-3 merged chlorophyll product (Franz et al.). 

Figure 9.2: Example of daily images (December 4, 2000) of chl (upper panel), acdm (443) (center panel) and 

bbp(443) (lower panel) generated by the GSM01 merging model using daily level-3 LwN(λ) data from

SeaWiFS and MODIS-Terra. 

The absence of marked discontinuities in the mapped product and the lower products uncertainties are 

two important results. At this point, it should be mentioned that some of the features of our merging 

approach cannot be used to their full efficiency mostly because the MODIS data are not fully characterized 

or stabilized. For example, the direct use of all available LwN data in the fitting procedure assumes they 

agree well and have similar or close uncertainty levels and that none of the data sources contains noticeable 

118 
SIMBIOS Project Annual Report

bias. This may not be always true. Although not used in the results presented here, the merging model has 

the ability of weighting each individual LwN(λ) data to insure that the best observations are given a higher 

weight in the fitting procedure that leads to the derivation of the retrievals. Uncertainties (σi(λj)) of input

LwN(λ) can be accounted for in the the least squares minimization (LSM) procedure as 

LSM =

L wN-i(λ j

)mod. - L wN-i(λ j)meas.

σ i(λ j)

 

 

 

 

 

 

2

j=1

Nλi

∑

i=1

Nsat

∑ (9.5) 

where Nsat is the number of data source and Nλ is the number of bands for each source. This has not yet 

been used mostly because the uncertainties associated with the MODIS bands of Terra and Aqua cannot yet 

be fully assessed in time and space. This requires matchup analyses from a large and diverse set of in situ 

and satellite data. These analyses are available for SeaWiFS but more matchup points are needed to 

complete the analysis for the MODIS data. It is also necessary to have some knowledge of the uncertainties 

variability in space and time. Once the characterization of Terra and Aqua is detailed enough, it should be 

possible to implement uncertainty weighting functions. A consistent BRDF correction scheme for the 

sensors involved in the data merging would also represent an improvement and upcoming reprocessings of 

SeaWiFS and MODIS data should take care of that particular aspect. 

sigma chl merged/sigma chl SeaWiFS sigma chl merged/sigma chl Terra

Figure 9.3: Frequency distribution of the ratio of the chl uncertainties using either SeaWiFS or MODISTerra LwN data alone (December 4, 2000) over the chl uncertainties when both sources are used (for the 

same pixels). Ratios < 1 show an improvement in the uncertainties of the merged products. 

119 
SIMBIOS Project Annual Report

n = 220277

slope = 0.943147

R2 = 0.871757

RMS = 0.1789

n = 160666

slope = 0.94197

R2 = 0.94197

RMS = 0.23784

n = 32212

slope = 0.98906

R2 = 0.713229

RMS = 0.345258

n = 52940

slope = 0.92971003

R2 = 0.75429

RMS = 0.2555

SeaWiFS Uncertainty Ratio 0.0-0.5 MODIS Uncertainty Ratio 0.0-0.5

SeaWiFS Uncertainty Ratio 0.5-1.0 MODIS Uncertainty Ratio 0.5-1.0

SeaWiFS (nLw 443)

MODIS-Terra (nLw 443)

SeaWiFS (nLw 443)

MODIS-Terra (nLw 443)

SeaWiFS (nLw 443)

MODIS-Terra (nLw 443)

SeaWiFS (nLw 443)

MODIS-Terra (nLw 443)

Figure 9.4: Comparison of LwN(443) data from SeaWiFS and MODIS-Terra data when uncertainties 

improved in the merged chl product (merged/unmerged uncertainty ratio of 0.5 or less, upper panels) and 

when they got worse (uncertainty ratio > 1, lower panels). 

In this project, we have demonstrated that ocean color data merging can effectively be conducted using 

LwN data and the inversion of a semi-analytical algorithm. The method can be applied straightforwardly to

any suite of ocean color sensors. Beside the feasibility aspect, the improvement in daily coverage and the 

lower uncertainties in the merged products are two important results of our work. Some refinements (e.g. 

weighting functions based on the uncertainty levels of the input LwN(λ) data, BRDF correction) can be 

added to the current approach in the future when some of the satellite data will be more mature 

120 
SIMBIOS Project Annual Report

0

5

10

15

20

25

30

35

Date

Merged Image

MODIS-Terra

MODIS-Aqua

SeaWiFS

Figure 9.5: Daily coverage resulting from the merging of SeaWiFS, MODIS-Terra and MODIS-Aqua for 

the 18 dates used in this study. The coverage is computed as the percentage of the total ocean area (between 

65 degrees North and 65 degrees South). 

REFERENCES 

Bates, D.M., and D.G. Watts, 1988: Nonlinear Regression Analysis and its Applications. Wily, 365 

pp.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. 

Fargion G.S. and C.R. McClain, 2003: SIMBIOS Project Annual Report 2002, NASA Technical

Memorandum, TM-2003-211622, NASA/GSFC, Greenbelt, Maryland, p157. 

Franz B.A., J.G. Wilding and J.M. Gales, 2003: Operational Merging of MODIS and SeaWiFS Ocean 

Color Products at Level-3. In "MODIS Validation, Data Merger and Other Activities by the SIMBIOS 

Project:2002-2003", Fargion G.S. and C.R. McClain, NASA Technical Memorandum, TM-2003-, 

NASA/GSFC, Greenbelt, Maryland, (In Press). 

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. 

Gregg, W.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. 

121 
SIMBIOS Project Annual Report

Gregg, W.W. and R.H. Woodward. 1998: Improvements in coverage frequency of ocean color: Combining 

data from SeaWiFS and MODIS. IEEE Transactions On Geoscience And Remote Sensing, 36 :1350-

1353. 

Kwiatkowska, E.J. & G.S. Fargion, 2003. Merger of ocean color data from multiple satellite missions 

within the SIMBIOS project. Ocean remote sensing and applications, R. Frouin, Y. Yuan and H. 

Kawamura [Eds.], Proceedings of SPIE, Vol. 4892, 168-182. 

McClain, C.R., W. Esaias, G. Feldman, R. Frouin, W. Gregg and S. Hooker. 2002: The Proposal for the 

NASA Sensor Intercalibration and Merger for Biological and Interdisciplinary Oceanic Studies 

(SIMBIOS) Program, 1995. NASA Technical Memorandum, TM-2002-210008, NASA/GSFC, 

Greenbelt, Maryland, p54.

Maritorena S., D.A. Siegel and A. Peterson. 2002: Optimization of a Semi-Analytical Ocean Color Model 

for Global Scale Applications. Applied Optics. 41(15): 2705-2714. 

Maritorena, S., D.A. Siegel and D.B. Court, 2003: Ocean Color Data Merging Using a Semi-Analytical 

Algorithm. In preparation for Remote sensing of Environment.

O'Reilly J. E., S. Maritorena, B. G. Mitchell, D. A. Siegel, K. L. Carder, S. A. Garver, M. Kahru and C. R. 

McClain. 1998. Ocean Color Chlorophyll Algorithms for SeaWiFS. J. Geophys. Res., 103(C11):

24,937-24,953. 

Siegel, D.A., S. Maritorena, N.B. Nelson, D.A. Hansell & M. Lorenzi-Kayser. 2002. Global Distribution 

and Dynamics of Colored Dissolved and Detrital Organic Materials. Journal of Geophysical Research. 

107(C12): 10.1029/2001JC000965. 

Siegel, D.A., S. Maritorena, N.B. Nelson, 2003: Independence and interdependencies of ocean optical 

properties viewed using SeaWiFS. In preparation for Journal of Geophysical Research. 

This Research was Supported by 

the NASA Contract # 00196 

Publications 

Behrenfeld, M.J., E. Boss, D.A. Siegel and D.M. Shea, 2003: Global remote sensing of phytoplankton 

physiology. Submitted to Science. 

Chomko, R.M., H.R. Gordon, S. Maritorena and D.A. Siegel, 2003: Simultaneous determination of oceanic 

and atmospheric parameters for ocean color imagery by spectral optimization: a validation. Remote 

Sensing of the Environment, 84, 208-220. 

Maritorena S., D.A. Siegel & A. Peterson. 2002: Optimization of a Semi-Analytical Ocean Color Model for 

Global Scale Applications. Applied Optics. 41(15): 2705-2714. 

Maritorena, S., D.A. Siegel and D.B. Court, 2003: Ocean Color Data Merging Using a Semi-Analytical 

Algorithm. In preparation for Remote sensing of Environment.

Morel A. and S. Maritorena. 2001: Bio-optical properties of oceanic waters: a reappraisal. J. Geophys. Res., 

106(C4) : 7163-7180. 

Siegel, D.A., S. Maritorena, N. B. Nelson, D.A. Hansell and M. Lorenzi-Kayser, 2002: Global ocean 

distribution and dynamics of colored dissolved and detrital organic materials. Journal of Geophysical

Research, 107, 3228, DOI: 10.1029/2001JC000965. 

122 
SIMBIOS Project Annual Report

Siegel, D.A., S. Maritorena, N.B. Nelson, 2003: Independence and interdependencies of ocean optical 

properties viewed using SeaWiFS. For Journal of Geophysical Research. 

123 
SIMBIOS Project Annual Report

Chapter 10 

The Marine Fast-Rotating Shadow-band Network: 

Status Report and New Retrieval Techniques 

Mark A. Miller, R.M Reynolds, A. Vogelmann and M.J. Bartholomew 

The Brookhaven National Laboratory, Earth Systems Science Division, New York 

10.1 INTRODUCTION 

The radiation transfer characteristics of certain types of aerosols and mixtures of aerosols found over 

the world’s oceans are poorly known. To simulate the radiative impacts of these aerosols requires detailed 

knowledge of five basic physical characteristics: the size distribution, chemical composition, optical 

thickness, single scattering albedo, and the asymmetry parameter. The single scattering albedo describes 

the total amount of incoming energy that is scattered by the aerosol particle and the asymmetry parameter 

describes the angular distribution of this scattered energy relative to the incident direction. Given this

information, it is possible to accurately simulate the interaction of these aerosols with incoming solar 

radiation and the radiant energy scattered into space from the upper layers of the ocean, which is known as 

the water-leaving radiance. At present, the atmospheric correction schemes that are applied to satellite 

ocean color measurements are based primarily on the results of radiation transfer codes, which are supplied 

information about the physical characteristics of the aerosols from measurements made in the late 70’s 

(Gordon and Wang, Shettle and Fenn). These aerosol measurements and the radiation-transfer-based 

atmospheric correction algorithms that result from them have been recently shown to be inadequate to 

describe the observed distribution of Angstrom Exponent that has resulted from a subset of the SIMBIOS 

measurements (Knoblespiesse et al., 2003). This implies that the aerosol models that are currently used to

develop atmospheric correction algorithms do not adequately describe the physical characteristics of some

of the aerosols that are present in the marine environment. 

At the onset of SIMBIOS, there were virtually no aerosol optical thickness measurements from over 

the world’s oceans. This lack of data was due mostly to a lack of appropriate instrumentation to make the 

measurements on a routine basis. To address this deficiency, SIMBIOS deployed newly developed off-theshelf hand held radiometers (Microtops) and funded the development of a marine shadow-band radiometer. 

A network of ship-mounted marine shadow-band radiometers (FRSRs) and broadband radiometers have 

been deployed over the past five years on several backbone ships, along with periodic ships of opportunity, 

as part of the atmospheric correction exercises conducted by SIMBIOS (Reynolds et al., 2002). These 

radiometers operate continuously and automatically during daylight hours. As a result of these 

developments and the SIMBIOS program, a relatively large database of aerosol optical thicknesses 

measured from ships at sea has been amassed. To date, there are over 150 independent cruises in the FRSR

database, ranging in duration from a week to nearly two years. Data have been collected from all three 

oceans, as well as during important field campaigns such as Aerosols99, INDOEX, Nauru99, ACE-Asia, 

and the Northeast Aerosol Characterization Experiment (Voss et al. 2001; Quinn et al., 2001). The 

database consists of over 140,000 individual measurements of the radiation budget over the world’s oceans, 

including approximately 50,000 measurements of aerosol optical thickness in clear and partly cloudy skies. 

The fundamental measurements made by the FRSRs in the network are the direct-normal irradiance 

and diffuse irradiance in six 10-nm wide channels that span the visible and near-infrared wavelengths (440 

nm, 500 nm, 610 nm, 660 nm, 870 nm, and 936 nm) and a broadband channel. These measurements are 

complemented by broadband solar and infrared irradiance using standard Eppley pyranometers and 

pyrgeometers. Most importantly, a thorough evaluation of FRSR measurement uncertainty has shown that

the accuracy of FRSR aerosol optical thickness measurements is approximately 0.02-0.03 (Miller et al., 

2003). It has also been shown that the accuracy of FRSR measurements of the Angstrom Exponent is 

124 
SIMBIOS Project Annual Report

strongly dependent on the aerosol optical thickness such that measurements in polluted air masses are more 

accurate than those made in clean maritime conditions. 

An important and, currently, untapped resource within the database is the potential ability of the FRSR 

diffuse irradiance field to enable retrievals of the single scattering albedo and asymmetry parameter, and to

evaluate atmospheric correction models. At present, a retrieval algorithm for the single scattering albedo 

has been developed and tested on FRSR data collected during ACE-Asia, as detailed in this report. 

Examples of model evaluation exercises using FRSR diffuse and global data have been reported in previous 

SIMBIOS TMs and will not be discussed here. The results of these two efforts are presented in this report. 

The report also provides a final summary of current status of the network and a summary of 

recommendations for future activities based on the past five years of operation.

10.2. RETRIEVING THE AVERAGE SINGLE SCATTERING 

ALBEDO USING THE FRSR 

This section details our initial attempt to retrieve information in addition to the aerosol optical 

thickness about the radiation transfer characteristics of the aerosol in the column. Specifically, we have 

worked with Andy Vogelmann, a NASA and ARM PI, to develop a new single-scattering albedo retrieval

scheme for the FRSR. While this work is in the development phase, we are relatively encouraged by our

early results. 

The ratio of direct-to-diffuse radiation for each FRSR channel is effectively an aggregate scattering 

property that combines the effects of the single-scattering albedo and the scattering phase function. The 

challenge is to untangle these two parameters for a given solar zenith angle and aerosol optical depth. This

problem was first addressed by Herman et al. (1975) and King and Herman (1979), who were able to

retrieve surface albedo and aerosol absorption using prescribed aerosol optical models. Recent work by 

Anikin et al. (2002) provide an elegant set of closed equations that parameterize the direct-diffuse ratio in

terms of the aerosol optical depth, single-scattering albedo, asymmetry parameter, and a set of tuned 

coefficients. 

We have tested these equations and found them to provide excellent agreement with Modtran multiple 

scattering radiative transfer calculations for the aerosol types within the Modtran database. However, 

inverting these equations to obtain the aerosol single-scattering albedo requires the aerosol asymmetry

parameter, which they obtain from a parameterization based on the Angstrom exponent. The 

parameterization captures the first-order size-dependence of the asymmetry parameter, but necessitates 

having sufficient prior knowledge of the aerosol refractive index, which again would be difficult for our 

application. Several innovative studies have developed methods for retrieving the aerosol single-scattering 

properties by inverting sky radiances obtained from specific angles relative to the Sun (e.g., the principle 

plane and almucantar) that have been extremely successful (Nakajima et al., 1996; Dubovik and King, 

2000; Devaux et al., 1998; Gordon and Zhang, 1995; Zhang and Gordon, 1997). However, it has been 

found to be very difficult to apply these techniques to ship data owing to the difficulty of scanning the 

required sky planes on a rolling and pitching platform (Smirnov et al., 2000). 

The fundamentals of our trial approach are based on the original direct-diffuse approach (Herman et

al., 1975; King and Herman, 1979) as rendered by Anikin et al. (2002). This approach will eventually be 

augmented by the generalized radiance considerations provided in the formulations by Devaux et al. 

(1998), Gordon and Zhang (1995) and Zhang and Gordon (1997), who developed their retrievals to be 

independent of the aerosol model information. Relative to other research, our retrieval problem is greatly

simplified owing to the low and relatively constant ocean surface albedo. As for Anikin et al. (2002), we 

will also use the Angstrom exponent, obtained from the direct-beam measurements, to obtain a general 

indication of the aerosol particle size (within a specific aerosol type). Fortunately, the retrieved singlescattering albedos do not appear overly sensitive to the asymmetry parameter. For example, sensitivity 

studies indicate that a ±0.05 uncertainty in the aerosol asymmetry parameter results in only a ± 0.02 

uncertainty in the retrieved single-scattering albedo. 

Results from an initial attempt to use the FRSR diffuse irradiance in a retrieval procedure are shown in

Figure 10.1. Results from the retrieval are compared with surface measurements of the single scattering 

albedo made on the Ron Brown during Ace-Asia. As the figure shows, the retrieval algorithm is

125 
SIMBIOS Project Annual Report

reasonably successful on days 95-100, 107 and 109, but fails on days 102, 103, and 104. The reasons for 

failure on these days are under investigation, but the overall results are quite encouraging. 

Figure 10.1: Results from a prototype retrieval of the single scattering albedo from FRSR data collected 

during aboard the R/V Ronald H. Brown ACE-Asia. The black diamonds are surface measurements of the 

single scattering albedo. The red pluses are the results of the FRSR retrieval algorithm and the blue crosses 

are the results of a retrieval based on a 0-3 km deep aerosol optical model. 

10.3 ANALYSIS OF A LONG-TERM FRSR DATA SET: THE 

EXPLORER OF THE SEAS

We have discussed the size and scope of the FRSR database in past NASA TMs. In this final TM, we 

shall concentrate on the analysis of the data from the Explorer of the Seas, which is a cruise ship operated 

by Royal Caribbean. The ship has a full complement of atmospheric and oceanic sensors operated by the 

University of Miami. We deployed an FRSR on the Explorer nearly two years ago. It has operated 

continuously during successive one-week transects and the FRSR unit has remained highly stable. We are 

in the process of analyzing the first year of the data set and some of the results are shown here. 

The observed relationship between wind direction and aerosol optical thickness is shown in Figure 

10.2. Each panel shows a 30-degree span in wind direction and the size of the plotted circle reflects the 

magnitude of the observed optical thickness in this range of wind directions. The observations clearly

show increased optical thicknesses when the winds have a northeasterly component, as compared to east or 

southeast. As expected, there are relatively few observations when the winds are westerly because the 

climatological wind direction contains an easterly component. The presence of African dust in the 

Caribbean has been known for decades, but the Explorer of the Seas data set suggests that the wind 

direction observed when the aerosol optical thickness is typically the largest is not southeasterly, as might

be expected if there was a straight flow from Africa. We are in the process of analyzing these data to 

126 
SIMBIOS Project Annual Report

Figure 10.2: Each subplot shows the aerosol optical thickness for each cloud-free observation along the 

cruise track of the Explorer of the Seas when the wind direction is observed to be within the listed 30-

degree passband, which is centered on the direction of the small plotted arrow. The radius of each plotted 

circle represents the magnitude of the observed aerosol optical thickness and the largest radii indicate the 

highest aerosol optical thickness. 

127 
SIMBIOS Project Annual Report

determine whether the aerosol that we are observing has the physical characteristics of African dust 

(Angstrom Exponent), or whether it is a mixture of different aerosol, some potentially generated locally. 

The Explorer of the Seas has a full complement of sensors include standard meteorological variables. 

Scatterplots of the relationship between aerosol optical thickness and relative humidity, temperature, and

winds are shown in Figure 10.3. It is surprising to find that the relative humidity appears to have little 

correlation with the aerosol optical thickness. We have observed the same lack of a predicted relationship 

in some of our other analyses, but they have been made on smaller data sets. While we are in the process 

of further processing these data and doing additional filtering, the indication is that relative humidity does 

not play a significant role in determining the optical thickness in the Caribbean. Examination of the true

wind direction appears to show two distinct populations of aerosol with the most polluted air clearly

associated with wind directions in the 0-100 degree range. No relationship between wind speed or 

temperature and aerosol optical thickness is indicated, although wind speed is the primary governing factor 

in determining the concentration of sea-salt in the marine atmosphere. 

These observations may be consistent with theory despite the lack of strong signals showing high 

correlations between wind speed and aerosol optical thickness and relative humidity and aerosol optical

thickness in this region. What is apparent is that the relative humidity and wind speed do no appear to vary

through a wide enough dynamic range to enable the signals to be observed in the tropics. This would 

appear to be an important attribute of the tropical marine environment for a number of reasons. It suggests 

that observations of relative humidity and wind may not significantly improve atmospheric correction 

algorithms in these tropical regions because they do not change enough to be of importance in selecting a 

model. From a climate perspective, it also suggests the direct aerosol effects in the topics may not be 

modulated by atmospheric water vapor as they may be in continental regions. 

Figure 10.3: Each subplot shows a scatter plot of 1-hour average meteorological variables and one-hour 

average aerosol optical thickness. 

128 
SIMBIOS Project Annual Report

10.4 CONCLUSIONS 

We have amassed a large and important database over the four years of operation of the FRSR

network. While the data requires careful analysis and quality control before summary conclusions can be

reached, we expect that the data set will prove to be extremely useful for the atmospheric and 

oceanographic communities. There is considerable value in the data set that has yet to be realized. Most 

notably, the focus of optical thickness must be broadened to include the other radiative parameters of 

interest, namely the single scattering albedo and the asymmetry parameter. As we have demonstrated, it

may be possible to retrieve one or both of these variables using FRSR data. This is the path forward with

regard to aerosol physics in the marine boundary layer. In our opinion, the utility of aerosol optical 

thickness measurements alone is limited because model improvements are only possible when the other 

aerosol radiative forcing variables are known, along with related data such as in situ chemical

measurements and meteorological variables. The other aspect of the data that is largely untapped is

information about cloud optical thickness over the world’s oceans. 

REFERENCES

Anikin, P., M.A. Sviridenkov, and E. V. Romashova, 2002: Estimation of Aerosol Single-Scattering 

Albedo Over ZSS from MFRSR Data. In Proceedings of the Twelfth Atmospheric Radiation 

Measurement (ARM) Science Team Meeting, April 8-12, St. Petersburg, Florida. 

(http://www.arm.gov/docs/documents/technical/ conf_0204/anikin-p.pdf). 

Devaux, C, A. Vermeulen, J.L. Deuze, P. Dubuisson, M. Herman, R. Santer, and M. Verbrugghe, 1998:

Retrieval of aerosol single-scattering albedo from ground-based measurements: application to

observational data. J. Geophys. Res., 103, 8753-8761. 

Dubovik, O, and M.D. King, 2000: A flexible inversion algorithm for retrieval of aerosol optical properties 

from Sun and sky radiance measurements. J. Geophys. Res., 105, 20673-20696. 

Herman, B.M., R.S. Browning, and J.J. De Luisi, 1975: Determination of the effective imaginary term of 

the complex refractive index of atmospheric dust by remote sensing: The diffuse-direct radiation 

method. J. Atmos. Sci., 32, 918-925. 

Gordon, H.R., and T. Zhang, 1995: Columnar aerosol properties over oceans by combining surface and 

aircraft measurements: simulations. Appl. Opt., 34 (24), 5552-5555. 

King, M.D., and B.M. Herman, 1979: Determination of the ground albedo and the index of absorption of 

atmospheric particulates by remote sensing. I. Theory. J. Atmos. Sci., 36, 163-173. 

Miller, M.A., M.J. Bartholomew, and R.M. Reynolds, 2003: The accuracy of marine shadow-band 

measurements of aerosol optical thickness and Angstrom exponent. J. Atmos. Ocean. Tech. 

(submitted). 

Nakajima, T, G. Tonna, R. Rao, P. Boi, Y. Kaufman, and B. Holben, 1996: Use of sky brightness 

measurements from ground for remote sensing of particulate polydispersions. Appl. Optics, 35 (15), 

2672-2686. 

P.K. Quinn, D.J. Coffman, T.S. Bates, T.L. Miller, J.E. Johnson, E.J. Welton, C. Neusüss, M.A.Miller, and 

P. Sheridan: Aerosol Optical Properties during INDOEX 1999, 2001: Means,Variability, and 

Controlling Factors, J. Geophys. Research, 107(D18), 10.1029/2000JD000037, 2002.

Reynolds, M.R., M.A. Miller, and M.J. Bartholomew, 2002: A fast-rotating, spectral shadowband 

radiometer for marine applications. J. Atmos. Ocean. Tech., Vol. 18, No. 2, pp. 200–214 

129 
SIMBIOS Project Annual Report

Smirnov, A., B.N. Holben, O. Dubovik, N.T. O'Neill, L.A. Remer, T.F. Eck, L. Slutsker, and D Savoie, 

2000: Measurement of atmospheric optical parameters on U.S. Atlantic coast sites, ships, and Bermuda 

during TARFOX. J. Geophys. Res., 105, 9887-9901. 

Vogelmann, A.M., Miller, M.A., Flatau, P.J., Markowitz, K.M., 2003: Aerosol radiative effects and singlescattering properties in the Tropical Western Pacific. 2003 ARM Science Team Meeting. 

Voss, K.J., E.J. Welton, P.K. Quinn, R. Frouin, M.A. Miller, and R.M. Reynolds, 2001: Aerosol optical 

depth measurements during the Aerosols99 experiment, J. Geophys. Research, 106, 20811-20820. 

Zhang, T., and H.R. Gordon, 1997: Columnar aerosol properties over oceans by combining surface and 

aircraft measurements: sensitivity analysis. Appl. Optics, 36 (12), 2650-2662. 

 

 

 

 

 

 

 

 

 

130 
SIMBIOS Project Annual Report

Chapter 11 

Bio-Optical Measurement and Modeling of the 

California Current and Southern Oceans 

B. Greg Mitchell 

Scripps Institution of Oceanography, University of California San Diego, La Jolla, 

California 

11.1 INTRODUCTION 

This SIMBIOS project contract has supported in situ ocean optical observations in the California 

Current, and in the north Pacific, Southern and Indian Oceans. Our principal goals are to validate standard 

or experimental ocean color products through detailed bio-optical and biogeochemical measurements, and 

to combine ocean optical observations with modeling to contribute to satellite vicarious radiometric 

calibration and algorithm development. In collaboration with major oceanographic ship-based observation 

programs (CalCOFI, JGOFS, AMLR, INDOEX, and ACE Asia) our SIMBIOS effort has resulted in data 

from diverse bio-optical provinces. For these global deployments we generate a methodologically

consistent data set encompassing a wide-range of oceanic conditions. We have initiated several 

collaborations with scientists in East Asian countries to study the complex Case-2 waters of their marginal

seas. Global data collected in recent years are routinely evaluated relative to our CalCOFI time-series. 

The combined database we have assembled now comprises more than 1000 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 water-leaving radiance estimated with OCTS, SeaWiFS, 

MODIS and GLI as well as bio-optical algorithms for chlorophyll pigments. During the past year we 

continued to process and quality control our data and carried out a detailed calibration/validation exercise 

in the California Current for the Japanese GLI instrument. For this GLI initialization effort, we coordinated

with the Mexico IMECOCAL program to get extra data to complement our CalCOFI data for the 

simultaneous April, 2003 cruises. The comprehensive data is utilized for development of standard and 

experimental algorithms. 

11.2 RESEARCH METHODS AND DATA

We continue to participate on CalCOFI cruises to the California Current System (CCS) for which we 

have an 8-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, 2002). CalCOFI data provide an 

excellent reference for evaluating our other global data sets (O’Reilly et al. 1998; Mitchell et al. 2002b, 

2002c). During the third year of our contract, we participated in 2 CalCOFI cruises, a cruise to the 

Southern Ocean (AMLR), and received data from colleagues in Korea and Hong Kong for East Asian 

coastal cruises. The April 2003 CalCOFI cruise (CAL0304) was coordinated with the simultaneous 

IMECOCAL cruise (IME0304) off Mexico. The combined April data are being used for a comprehensive 

campaign for calibration and validation of GLI algorithms. A preliminary analysis of the water-leaving 

radiances and fluorometric chlorophyll concentration was presented at the SPIE 2003 conference 

(Fukushima et al., 2003) and at the subsequent meeting at Scripps Institution of Oceanography on August 

7, 2003. Work on the GLI calibration/validation using the combined dataset is continued . 

The global distribution of our data collected during 6 years of SIMBIOS funding is shown in Figure 

1.11. On most cruises, an integrated underwater profiling system was used to collect optical data and to 

131 
SIMBIOS Project Annual Report

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) 

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 

Figure 11.1: Global distribution of bio-optical stations accomplished in the past 5 years by the Scripps 

Photobiology Group (SPG). All stations include spectral reflectance and fluorometric chlorophyll. Most

include particle and soluble absorption and HPLC pigments. For many cruises since 1997 we have 

deployed Hydroscat backscatter and AC9 absorption and attenuation meters to better understand the 

variables that govern remote sensing reflectance. 

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). For two 

AMLR, ACE Asia and recent CalCOFI cruises we deployed our new Biospherical Instruments PRR-800 

freefall radiometer that included 19 channels of surface irradiance (312-865 nm) and three geometries of 

underwater radiometry (Lu, Eu, Ed) from 312 to 700 nm. 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). Our colleagues in Hong Kong and Korea use PRR-800 profilers and collect

samples for chlorophyll and absorption with methods consistent with ours. At in situ optical stations 

discrete water samples were collected from a CTD-Rosette immediately before or after each profile for 

additional optical and biogeochemical analyses. Pigment concentrations were determined fluorometrically 

and with HPLC. All HPLC samples acquired in the past year have been submitted to San Diego State 

University for analysis under a separate SIMBIOS contract. 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 (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. 

132 
SIMBIOS Project Annual Report

Absorption methods are described in more detail in Mitchell et al. (2002a). For some cruises we collected 

measurements of other optical and phytoplankton properties including photosynthesis, particulate organic 

matter (carbon and nitrogen), phycoerythrin pigment, and size distribution using flow cytometry and a 

Coulter Multi-sizer. 

For most of our field work in association with CalCOFI and AMLR we submit all data directly to

NASA SIMBIOS including supporting CTD and water bottle information. For the collaborations with 

Asian colleagues, we process their optical data and with their permission we will submit some of the Asian

data to SIMBIOS in 2003. On the SOFEX cruise to the Southern Ocean we did not deploy a profiling 

radiometer, but we collected data for particle absorption; other SOFEX PIs collected samples for 

fluorometric chlorophyll and HPLC pigments. Our absorption data from SOFEX will be submitted to 

SIMBIOS and we will request approval to submit the pigment data. 

Figure 11.2: Validation of GLI water-leaving radiances (nLw) at 443, 490 and 565 nm, and of the empirical

OC4-GLI chlorophyll algorithm during CalCOFI 0304 cruise. The in situ nLw were measured with the 

PRR-800. The GLI match-ups were estimated both as the value of the nearest pixel (o) and as average for a 

5 x 5 pixel area (*) centered at the nearest pixel. The chl-a match-ups show very good performance of the 

OC4-GLI algorithm. The only station with a significant difference was a near-shore station with expected 

problems like small-scale spatial variability, terrestrial aerosols and stray light. 

133 
SIMBIOS Project Annual Report

Figure 11.3: Validation of SeaWiFS water-leaving radiance spectra with three match-ups within a 3-hour 

time lag during the CalCOFI 0304 cruise. At each station two casts of PRR-800 were made to get in situ 

nLw spectra between 313 and 700 nm (continuous line with X). The SeaWiFS nLw spectra were estimated 

with SeaDAS version 4.4 and the default options. While at longer wavelengths the SeaWiFS-derived nLw 

values are within the in-station variability range of in situ values, at 412 and even at 443 nm SeaWiFS is 

still under-predicting the in situ nLw. It should be noted that all three match-ups were near-shore and had 

high to very high chl-a concentration (7.45, 2.23 and 13.74 mg m-3). At near-shore stations the 

discrepancies may be caused by small-scale spatial variability, terrestrial aerosols and stray light. In fact, 

the large small-scale variability in in situ nLw is shown at the bottom panel. 

134 
SIMBIOS Project Annual Report

11.3 RESEARCH RESULTS

Remote sensing reflectance (Rrs) derived from the in-water measurements, defined as the ratio of 

upwelling radiance (Lu) and downwelling irradiance (Ed), is closely related to the normalized waterleaving radiance (Lwn) product of satellite ocean color data. Rrs (λ) in Case-1 waters is well correlated to

chl-a. As chl-a increases, Rrs generally decreases for SeaWiFS wavelengths 412, 443, 490, 510 nm due to

increased absorption, but increases at SeaWiFS wavelength 555 and 665 nm due to increased backscatter 

(Mitchell and Kahru, 1998; O’Reilly et al., 1998). To explore issues with Case-2 waters we have 

collaborated with colleagues in Korea (NFRDI), and Hong Kong (HKUST) to assemble a large regional

data set from the East China Sea, South China Sea and coastal waters of Eastern Asia. Relationships

between Rrs and chl-a for HKUST and NFRDI data can be both lower and higher than the Case-1 

(CalCOFI) relationships, depending on whether absorption or scattering is the dominant process (data not

shown). 

Typical ocean color algorithms take ratios of reflectance for different spectral bands. The standard 

SeaWiFS chl-a algorithm is currently OC4v4 [O’Reilly et al., 1998; O’Reilly et al., 2000]. CalCOFI data 

set is a good reference data set since it covers a large range of chl-a, was acquired with very consistent

methodology, and comprises approximately 25% of the data used for development of the SeaWiFS OC4v4 

algorithm (O’Reilly et al., 2000). Kahru and Mitchell (1999; 2001) have shown that the chl-a retrieved by 

satellite in the CalCOFI region is in good agreement with our ship data when computed with our regional 

algorithm or NASA’s OC4v4. Thus the CalCOFI region is an ideal location to evaluate new ocean color 

missions. We collaborated closely with Japanese colleagues and NASDA to execute a comprehensive 

calibration and validation program for GLI on the April CalCOFI and IMECOCAL cruises. The GLI 

empirical chlorophyll algorithm OC4-GLI (Mitchell and Kahru, unpublished) that uses the same maximum

band ratio algorithm as OC4v4 of SeaWiFS (O’Reilly et al., 1998) but a different set of bands (443, 460, 

520, 545 nm) seems to be working very well (Figure 11.2). The ratio of GLI-detected chlorophyll to

SeaWiFS-detected chlorophyll (not shown) is close to one. The GLI water-leaving radiances correspond 

well to in situ measurements (Figure 2), especially considering the early phase of the cal/val process. 

However, we are still seeing significant discrepancies between satellite-derived nLw and in situ values at 

near-shore CalCOFI stations (Figure 11.3). While the negative water-leaving radiances have disappeared in

our match-ups, the SeaWiFS-derived nLw412 and even nLw443 are still often lower than in situ values. 

The maximum band ratio algorithms compensate for the decreased nLw values at 412 and 443 nm by

switching to longer wavelength bands but the distortion in the satellite-derived nLw spectra causes severe 

problems for semi-analytic algorithms. 

 11.4 CONCLUSIONS 

Since this is the final year of NASA SIMBIOS, NASA should define an explicit program for in situ

support of ocean color satellites. Even for Case 1 waters, algorithm problems persist and this will be the 

focus of a session at the ASLO Ocean Sciences meeting in 2004. Our SIMBIOS data have demonstrated 

that the Southern Ocean should continue to be a high priority since it is evident that standard algorithms

for chl-a do not perform well in this region (Mitchell and Holm-Hansen, 1991; Mitchell, 1992; Mitchell et

al., 2002b; Reynolds et al., 2001). Furthermore, it is essential to broaden the in situ data sets to include 

detailed information on spectral absorption and backscattering, particle size distributions, mineral and 

organic mass, and the optical differentiation of bio-geochemically important taxa like diazotrophs, diatoms 

and coccolithophores. To understand the details of how community structure regulates spectral reflectance 

so that advanced algorithms can be developed requires a commitment to supporting detailed in situ studies. 

REFERENCES

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 nitratedepletion temperatures. Journal of Geophysical Research. 104(C3): 5,403-5,421 

135 
SIMBIOS Project Annual Report

Fukushima, H., M. Toratani, A. Tanaka, W.-Z. Chen, H. Murakami, R. Frouin , G. Mitchell and M. Kahru, 

2003: ADEOS-II/GLI ocean color atmospheric correction: early phase results. SPIE 48th Annual 

Meeting, San Diego, August 5, 2003. 

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 

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, 2001: Seasonal and non-seasonal variability of satellite-derived chlorophyll 

and colored dissolved organic matter concentration in the California Current. Journal of Geophysical

Research. 106(C2): 2517-2529 

Kahru, M., B.G. Mitchell, J. Wieland, J.C. Chen, N.W. Man, Y.S. Suh, M. Stramska, and D. Stramski, 

2002: Evaluation of Satellite Ocean Color Algorithms for East Asian Marginal Seas. ASLO, Honolulu, 

Hawaii, February 2002.

Kahru, M. and B.G. Mitchell, 2002: Influence of El Niño – La Niña cycle on satellite-derived primary 

production in the California current. Geophysical Research Letters, 29(9), doi: 

10.1029/2002GL014963 

Li, L.-P., H. Fukushima, R. Frouin, B.G. Mitchell, M. -X. He, I. Uno, T. Takamura, and S. Ohta (In Press) 

Influence of sub-micron absorptive aerosol on SeaWiFS-derived marine reflectance during ACE-Asia. 

Journal of Geophysical Research – Atmospheres.

Loisel, H., D. Stramski, B.G. Mitchell, F. Fell, V. Fournier-Sicre, B. Lemasle, and M. Babin, 2001:

Comparison of the ocean inherent optical properties obtained from measurements and inverse 

modeling. Applied Optics. 40(15): 2384-2397 

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. Deep-Sea Research I. 38(8/9): 1,009-1,028. 

Mitchell B. G., M. Kahru, J. Wieland and M. Stramska, 2002a: Determination of spectral absorption 

coefficients of particles, dissolved material and phytoplankton for discrete water samples. In: Fargion, 

G.S. and J.L. Mueller, Eds. Ocean Optics Protocols for Satellite Ocean Color Sensor Validation, NASA

Technical Memorandum 2002-210004/Rev 3-Vol 2, Chapter 15, P. 231-257. 

Mitchell, B.G., M. Kahru, R. Reynolds, J. Wieland, D. Stramski, C. Hewes, and O. Holm-Hansen, 2002b:

Chlorophyll-a Ocean Color Algorithms for the Southern Ocean and their Influence on Satellite 

Estimates of Primary Production. ASLO, Honolulu, Hawaii, February 2002. 

136 
SIMBIOS Project Annual Report

Mitchell, B.G., M. Kahru, J. Wieland, and H. Wang, J.C. Chen, N.W. Man, and Y.S. Suh, 2002c: Case 2 

Optical relationships for East Asian marginal seas. Ocean Optics XVI Conference. Santa Fe, New 

Mexico, November 18–22, 2002. 

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. 

Reynolds, R.A., D. Stramski and B.G. Mitchell, 2001: A chlorophyll-dependent semianalytical reflectance 

model from derived from field measurements of absorption and back scattering coefficients within the 

Southern Ocean. Journal of Geophysical Research. 106(C4): 7125-7138. 

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. 

137 
SIMBIOS Project Annual Report

Chapter 12 

Variability in Ocean Color Associated with 

Phytoplankton and Terrigenous Matter: Time Series 

Measurements and Algorithm Development at the 

FRONT Site on the New England Continental Shelf. 

John R. Morrison and Heidi M. Sosik 

Woods Hole Oceanographic Institution, Woods Hole, Massachusetts 

12.1 INTRODUCTION 

Fronts in the coastal ocean describe areas of strong horizontal gradients in both physical and biological 

properties associated with tidal mixing and freshwater estuarine output (e.g. Simpson, 1981 and O’Donnell,

1993). Related gradients in optically important constituents mean that fronts can be observed from space as 

changes in ocean color as well as sea surface temperature (e.g., Dupouy et al., 1986). This research 

program is designed to determine which processes and optically important constituents must be considered

to explain ocean color variations associated with coastal fronts on the New England continental shelf, in 

particular the National Ocean Partnership Program (NOPP) Front Resolving Observational Network with 

Telemetry (FRONT) site. This site is located at the mouth of Long Island sound and was selected after the 

analysis of 12 years of AVHRR data showed the region to be an area of strong frontal activity (Ullman and 

Cornillon, 1999). FRONT consists of a network of modem nodes that link bottom mounted Acoustic

Doppler Current Profilers (ADCPs) and profiling arrays. At the center of the network is the Autonomous 

Vertically Profiling Plankton Observatory (AVPPO) (Thwaites et al. 1998). The AVPPO consists of 

buoyant sampling vehicle and a trawl-resistant bottom-mounted enclosure, which holds a winch, the 

vehicle (when not sampling), batteries, and controller. Three sampling systems are present on the vehicle, 

a video plankton recorder, a CTD with accessory sensors, and a suite of bio-optical sensors including 

Satlantic OCI-200 and OCR-200 spectral radiometers and a WetLabs ac-9 dual path absorption and 

attenuation meter. At preprogrammed times the vehicle is released, floats to the surface, and is then 

winched back into the enclosure with power and data connection maintained through the winch cable. 

Communication to shore is possible through a bottom cable and nearby surface telemetry buoy, equipped 

with a mobile modem, giving the capability for near-real time data transmission and interactive sampling 

control.

12.2 RESEARCH ACTIVITIES 

AVPPO deployments 

The AVPPO was deployed during 2003 at the Martha’s Vineyard Coastal Observatory (MVCO) as 

part of the Coastal Carbon Time-series (CCTS, J. Campbell and D. Vandemark – PIs) project of the Coastal

Ocean Observation and Analysis (COOA) center of the University of New Hampshire (UNH). The 

Martha’s Vineyard Coast Observatory, which came on-line in June 2001, was designed as a highly flexible

facility to provide investigators, from school students to scientists, the means to collect long-term

environmental data. It is located off the south shore of the island Martha’s Vineyard on the northeastern 

seaboard of the U.S. between the highly productive waters of the Gulf of Maine (GOM) and the Mid 

Atlantic Bight (O'Reilly et al. 1987). The cabled facility provides power and communications at an 

underwater node and at the Air Sea Interaction Tower (ASIT), which is 3 km offshore in 15 m of water. 

Fast Ethernet communications and large power supply facilitates high bandwidth realtime sampling which 

138 
SIMBIOS Project Annual Report

is not possible in moorings traditionally used for coastal measurements. The AVPPO was reconfigured to

take advantage of the MVCO infrastructure with power and telemetry obtained from the ASIT and 

deployed on 

 

Figure 12.1: Processes at MVCO from AVPPO measurements between 4 June and 8 July 2003. A) The 

onset of thermal stratification as indicated by interpolated temperature values. High wind (B) events

apparently mixed the water column. Stratification intensified with high solar irradiance (C) and low winds

after day 172. Expansion of the data between Day 179 and 182 (D-F) shows warm water intrusions at tidal

frequencies together with particulate resuspension events (beam attenuation coefficient at 440 nm). 

Resuspension appears correlated with periods of strong bottom current speeds. 

May, 30 2003 at 41 19.412 °N, 70 33.954 °W in approximately 15 m of water. Realtime processing of data 

was implemented and contour plots are automatically updated at the AVPPO website

(http://4dgeo.whoi.edu/vpr).

12.3 RESEARCH RESULTS 

Ongoing observations from the AVPPO illustrate some of the important forcing on the coastal ocean 

around MVCO. Vertical temperature profiles clearly illustrate warming and the onset of summer 

stratification modified by mixing induced by surface winds (Fig. 12.1). Initial analysis of beam attenuation 

coefficient, c (λ), measured from the AVPPO during this time period also illustrated the effects of tidal 

forcing. Tidal currents resuspended bottom sediment and appeared to advect differing water masses over 

the study sight (Fig. 12.1). On maintenance cruises these fronts were clearly visible as distinct lines of 

surface debris. Wind driven resuspension of sediment is also an important process in coastal oceans and 

has been observed in previous deployments of the AVPPO in New England coastal waters (Morrison and 

Sosik 2002a) and in deeper waters south of Martha’s Vineyard (Chang 2001). 

139 
SIMBIOS Project Annual Report

Using solar stimulated fluorescence (SSF) Morrison (2003) presented evidence of three types of 

quenching in natural phytoplankton populations, 1) photochemical quenching, 2) rapidly varying energy

dependent quenching, qE, of which the xanthophyll cycle is an example (Demmig-Adams and Adams 

1992), and 3) quenching associated with photoinhibition, qI, with longer relaxation timescales (Horton et 

al. 1996). The last two are examples of non-photochemical quenching. 

Figure 12.2: Quantum yield of fluorescence proxy from the 25 June – 1 July, 2003. A) Surface φf values 

demonstrated non-photochemcial quenching decreasing φf during the majority of daytime. Day 180 clearly

shows this modulated by surface irradiance. B) Decreases in φf at low irradiances were indicative of 

photochemical quenching (blue – all depths, red – surface data from A). C) Biomass normalized fluoresce 

– irradiance showed similar relationships to those reported previously (e.g., Schallenberg et al. 2002) 

Similar trends were also observed at MVCO during the summer of 2003. Proxy values of the quantum

yield of fluorescence (upwelling radiance at 665 nm used to correct for back- and Raman scattered light) 

were calculated from radiometric measurements from the AVPPO with the formulations of Morrison 

(2003, phytoplankton absorption was obtained from ac-9 measurements). The fluorescence yield was 

suppressed at high irradiances in the middle of the day and responded to varying irradiance conditions 

induced by clouds (Fig. 12.2A). The transition from photochemically to non-photochemically dominated 

quenching was apparent both at fixed depths and throughout the water column (Fig. 12.2A and B). To our 

knowledge, these observations represent one of the most detailed studies of the diurnal variability of the 

fluorescence yield of natural phytoplankton populations to date. 

We have shown previously that absorption spectra measured with an ac-9 can be decomposed into that 

from phytoplankton and non-algal material (Morrison and Sosik 2002a). This was achieved by assigning 

characteristic spectral shapes to each fraction and iteratively varying constituent concentrations to minimize 

the sum of square deviations between observed and predicted spectra. The spectral slope of non-algal

material, S, was also iteratively varied to account for different spectral shapes but the phytoplankton 

absorption shape remained constant. Ciotti et al. (2002) described the phytoplankton absorption shape as a 

mixing series between two normalized spectra representative of pico- and micro-phytoplankton or minimal

and maximal packaging, âph

pico(λ) and âphmicro(λ), respectively. Greater than 80% of the variability in the 

spectral shape of phytoplankton was explained using this method. This parameterization of the 

phytoplankton absorption shape was included in the previous absorption model and used in the 

140 
SIMBIOS Project Annual Report

decomposition. To test the ability of the method to accurately reproduce spectral variation 478 groups of 

absorption spectra of particles, non-algal particles, and colored dissolved organic material (ap(λ), aNAP(λ), 

and aCDOM(λ), respectively) from the North East Atlantic were used to assess the absorption decomposition

methods (Morrison and Sosik in prep). Phytoplankton absorption, aph(λ), was calculated as the difference 

between ap(λ) and aNAP(λ). Spectra were measured with bench-top spectrophotometers either by ourselves 

or as part of the Bermuda Bio-Optics Project (Siegel et al. 1995) using standard protocols (Mitchell et al. 

2000) and are archived at the NASA Seabass database. 

1 2 3 4 5

1

2

3

4

5

Measured a

ph(440)/aph(675)

Predicted aph(440)/aph(675)

A

0.005 0.01 0.015 0.02 0.025

0.005

0.01

0.015

0.02

0.025

Measured S

dm (nm−1)

Predicted 

Sdm (nm−1)

C

0 0.2 0.4 0.6 0.8 1

0

0.2

0.4

0.6

0.8

1

Spectrophotometer Ciotti factor

ac−9 Ciotti factor

B

Figure 12.3: Spectral information of the original data was also retrieved by the inversion. (A) The ratio of 

the phytoplankton absorption maxima, aph(440)/aph(675), from the inversion was significantly related to

that from the original aph(λ) when aph(440)/anw(440) ≥ 0.40, r2 = 0.77 N=135. (B) The ciotti factor from

the inversion was significantly related to that obtained from the original phytoplankton spectra, same data

as in 3A r2 = 0.87. (C) Spectral shapes of non-algal absorption were also retrieved as indicated by the 

significant relationship between the slope, Sdm, r2 = 0.77 for all data. 

141 
SIMBIOS Project Annual Report

Comparison of the ratios of the phytoplankton absorption, aph(440)/aph(675), and the Ciotti factors, fc, 

retrieved from the inversion with those calculated from the original phytoplankton absorption spectra 

indicated that spectral variations in aph(λ) were retrieved with the inversion, Figure 12.3A and B. Similarly,

comparison of the spectral slopes of the non-algal absorption from the inversion with those calculated with

the original data demonstrated that the inversion retrieved spectral variation of adm(λ), Figure 12.3C. 

Babin et al. (2003) showed theoretical normalized scattering spectra for both phytoplankton and nonalgal particles which varied with particle size and refractive index. Theoretically, scattering shapes for 

non-algal particles can be described as a power law with slope γ which increases with decreasing particle 

size. The exponent γ is related to the Junge exponent of the particle size distribution, j, through γ = j - 3 

(Babin et al. 2003; Boss et al. 2001; Morel 1973). Inclusion of absorbing bands, such as with

phytoplankton, is accompanied by decreases in the scattering coefficient. Evidence of the variation in the 

range of scattering shapes was present in the 25,276 one meter depth-binned ac-9 measurements obtained 

with the AVPPO deployments prior to 2003 and the 5 minute time-binned data from the cruise EN372 (Fig. 

12.4, Morrison and Sosik 2002b). Further analysis demonstrated that greater than 99 % of the variation in

the spectral scattering shape could be explained by three normalized scattering spectra, small and large 

non-algal particles (slopes of 1.63 and -0.20 nm-1, respectively) and large phytoplankton. The spectral

shape of small phytoplankton was indistinct from the others and was similar to small particles mixed with

large phytoplankton. 

400 450 500 550 600 650 700 750

0.6

0.7

0.8

0.9

1

1.1

1.2

1.3

1.4

small phyto

large phyto

small NAP

large NAP

fitted

Figure 12.4: The normalized scattering spectra for both small and large phytoplankton and non-algal

particles (NAPs) determined from AVPPO ac-9 measurements prior to 2003. Note the troughs in the 

phytoplankton scattering spectra associated with absorption peaks. The slopes of the fitted values are 1.136 

and 0.012 nm-1 for the small and large NAPs, respectively.

12.4 CONCLUSIONS

Parameterizing the variability in inherent optical properties associated with optically important

constituents is fundamental to understanding variations in ocean color, especially in optically complex 

regions such as the coastal ocean. Measurements with sufficient resolution, both temporal and spatial, are 

necessary to encompass the range of possible IOPs and to understand their forcings. Much of this detail

has been captured by the Autonomous Vertically Profiling Plankton Observatory during deployments at the 

FRONT site, in Massachusetts Bay, and at the MVCO. We have explained the majority of the variation in

142 
SIMBIOS Project Annual Report

the measured IOPs by developing models based on characteristic shape vectors. We are currently working 

to improve ocean color remote sensing inversion algorithms in Case II by including this increased 

understanding of the natural range of IOPs. This includes approaches utilizing the increased information 

associated with hyperspectral measurements. 

Rigorous use of solar stimulated fluorescence depends on a quantitative understanding the variability 

of the quantum yield of fluorescence of phytoplankton. Inherent in this is determining the rates of onset

and relaxation of non-photochemical quenching. As demonstrated by the data presented herein (Fig. 12.3), 

the Autonomous Vertically Profiling Plankton Observatory provides an ideal platform for such a study of 

solar stimulated fluorescence. 

REFERENCES 

Babin, M., A. Morel, V. Fournier-Sicre, F. Fell, and D. Stramski. 2003: Light scattering properties of 

marine particles in coastal and open ocean waters as related to the particle mass concentration. 

Limnology and Oceanography 48: 843-859. 

Boss, E., M. S. Twardowski, and S. Herring. 2001: Shape of the particulate beam attenuation spectrum and 

its inversion to obtain the shape of the particulate size distribution. Applied Optics 40: 4885-4893. 

Chang, G. C. 2001: Sediment resuspension over a continental shelf during Hurricanes Edouard and 

Hortense (vol 106, pg 9517, 2001). Journal of Geophysical Research-Oceans 106: 19995-19995. 

Ciotti, A. M., M. R. Lewis, and J. J. Cullen. 2002: Assessment of the relationships between dominant cell 

size in natural phytoplankton communities and the spectral shape of the absorption coefficient. 

Limnology and Oceanography 47: 404-417. 

Demmig-Adams, B., and W. W. I. Adams. 1992: Photoprotection and other responses of plants to high 

light stress. Annual Review of Plant Physiology and Plant Molecular Biology 43: 599-626. 

Horton, P., A. V. Ruban, and R. G. Walters. 1996: Regulation of light harvesting in green plants. Annual

Review of Plant Physiology and Plant Molecular Biology 47: 655-684. 

Mitchell, B. G. and others 2000: Determination of spectral absorption coefficients of particles, dissolved 

material and phytoplankton for discrete water samples. In G. S. Fargion and J. L. Mueller [eds.], Ocean 

optics protocols for satellite ocean color sensor validation, revision 2, NASA Technical Memoradum, 

2000-209966. NASA. 

Morel, A. 1973: Diffusion de la lumiere par les eaux de mer: Resultats experimentaux et approche 

theorique, p. 3.1.1-3.1.76, Optics of the sea. AGARD lecture series. North Atlantic Treaty

Orginisation. 

Morrison, J. R. 2003: In situ determination of the quantum yield of phytoplankton chlorophyll a

fluorescence: a simple algorithm, observations, and a model. Limnology and Oceanography 48: 618-

631. 

Morrison, J. R., and H. M. Sosik. 2002a: Inherent optical properties in New England coastal waters:

decomposition into contributions from optically important constituents, p. 1-10, Ocean Optics XVI. 

Morrison, J. R., and H. M. Sosik. 2002b: Variability in ocean color associated with phyoplankton and 

terrigenous matter: time series measurements and algorithm development at the FRONT site on the 

New England continental shelf, p. 91-97. In G. S. Fargion and C. R. McClain [eds.], SIMBIOS project

2002 annual report. NASA. 

143 
SIMBIOS Project Annual Report

Morrison, J. R., and H. M. Sosik. in prep. Analysis of nine channel absorption-attenuation meter data:

Scattering correction and decomposition of constituent contributions to absorption and scattering. to be

submitted to Limnology and Oceanography Methods. 

Schallenberg, C., M. R. Lewis, D. E. Kelley, and J. J. Cullen. 2002: Variability in the quantum yield of suninduced fluorescence in the Bering Sea: effects of light and nutrients, p. 1-13, Ocean Opticx, XVI. 

Siegel, D. A., A. F. Michaels, J. C. Sorensen, M. C. O'Brien, and M. A. Hammer. 1995: Seasonal

variability of light availability and utilization in the Sargasso Sea. Journal of Geophysical Research 

100: 8695-8713. 

This Research was Supported by 

the NASA Contract # 00198 

Publications 

Morrison, J. R. 2003: In situ determination of the quantum yield of phytoplankton chlorophyll a

fluorescence: a simple algorithm, observations, and a model. Limnology and Oceanography 48: 618-

631. 

Morrison, J. R., and N. B. Nelson. 2003: Seasonal cycle of phytoplankton UV absorption at the Bermuda 

Atlantic Time-series Study (BATS) site. Limnology and Oceanography (in press). 

Presentations 

Morrison, J.R., Sosik, H.M., 2002: Inherent optical properties in New England coastal waters:

Decomposition into contributions from optically important constituents. Presented at Ocean Optics 

XVI, Santa Fe, NM. 

144 
SIMBIOS Project Annual Report

Chapter 13 

Ocean Optics Protocols and SIMBIOS Protocol 

Intercomparison Round Robin Experiments 

(SPIRREX). 

James L. Mueller 

Center for Hydro-Optics and Remote Sensing, San Diego State University, California 

13.1 INTRODUCTION 

The objective of research under this contract is the maintenance and annual revision of the document

“Ocean Optics Protocols for Satellite Ocean Color Sensor Validation (e.g. Fargion and Mueller 2000; 

Mueller et al. 2002; Mueller et al. 2003a – 2003e; Pegau et al. 2003). 

13.2 RESEARCH ACTIVITIES 

The research activities under the third year of this contract were devoted to completing final touches on 

Vol. IV (Pegau et al. 2003) and Vol. VI of “Ocean Optics Protocols for Satellite Ocean Color Validation, 

Revision 4” (Mueller et al. 2002), and in the preparation and completion of Revision 5. Revision 5 consists

of a small number of important corrections to some of the volumes of Revision 4, a revised Volume V 

containing one new chapter, and a new Volume VI, Part 2 containing two new “Special Topics” chapters. 

The important updates, additions and revisions in the Revision 5 document are summarized in the next 

section. 

13.3 RESEARCH RESULTS 

Revision 4 to the ocean optics protocol document was reorganized, from the format of Revision 3 

(Mueller et al. 2000a) into 6 separate volumes (Mueller et al. 2003a through 2003e, Pegau et al. 2003). 

This multi-volume format was adopted to allow timely future protocol revisions to be made reflecting 

important evolution of instruments and methods in some areas, without reissuing the entire document.

Revision 5 consists of Errata to these Volumes (published in PDF form on the SIMBIOS Web Page), a 

revised version of Volume V (Biogeochemical and Bio-Optical Protocols) and a new Volume VI, Part II 

(Special Topics). Revision 5 to Vol. V contains corrections to the HPLC Pigment Protocols (Chapter 2) 

and a new Chapter 4 covering protocols for measurements of scattering and Calcium Carbonate

concentrations associated with coccolithophorids. Volume VI, Part 2, contains two new chapters:

“Advances in Radiometry for Ocean Color” and “Progress in Self Shading Corrections for In-Water 

Measurements of Upwelled Radiance”. 

13.4 CONCLUSIONS 

Together, Revisions 4/5 comprise the final version of the “Ocean Optics Protocols for Satellite Ocean 

Color Sensor Validation” that will be issued under the auspices of the NASA SIMBIOS and SeaWiFS 

Programs. The scope and detail of this document has, at this stage, increased enormously over the decade 

since the first version was issued (Mueller and Austin 1992), as evidenced by comparing the slim 45pp of 

that first document to the more than 450 pages of the multi-volume present version. The present version 

also benefits greatly from the direct involvement of a broader authorship. 

145 
SIMBIOS Project Annual Report

There are currently no plans for continued evolution of the Ocean Optics Protocols. It is hoped that at 

some point in the next few years, someone will revisit and update them, for they will always be a “work in 

progress”. 

REFERENCES 

Fargion, G.S. and J.L. Mueller, 2000: Ocean Optics Protocols for Satellite Ocean Color Sensor Validation, 

Revision 2, NASA TM 2001-209955, NASA Goddard Space Flight Center, Greenbelt, Maryland, 184 

pp. 

Mueller, J.L. and R.W. Austin, 1992: Ocean Optics Protocols for SeaWiFS Validation. NASA Tech. 

Memo. 104566, Vol. 5, S.B. Hooker and E.R. Firestone (Eds.), NASA Goddard Space Flight Center, 

Greenbelt, Maryland, 45pp. 

Mueller, J.L., et al., 2002: Ocean Optics Protocols for Satellite Ocean Color Sensor Validation, Revision 3, 

NASA TM 2002-210004, Mueller, J.L. and G. Fargion (Eds.), NASA Goddard Space Flight Center, 

Greenbelt, Maryland, 308pp. 

Mueller, J.L., et al., 2003a: Ocean Optics Protocols for Satellite Ocean Color Sensor Validation, Revision 

4, Volume I: Introduction, Background and Conventions. NASA TM 2003-211621/Rev.4-Vol.I, 

Mueller, J.L., G. Fargion and C.R. McClain (Eds.), NASA Goddard Space Flight Center, Greenbelt, 

Maryland, 50pp. 

Mueller, J.L., et al., 2003b: Ocean Optics Protocols for Satellite Ocean Color Sensor Validation, Revision 

4, Volume II: Instrument Specifications, Characterization, and Calibration.. NASA TM 2003-

211621/Rev.4-Vol.II, Mueller, J.L., G. Fargion and C.R. McClain (Eds.), NASA Goddard Space Flight 

Center, Greenbelt, Maryland, 56pp. 

Mueller, J.L., et al., 2003c: Ocean Optics Protocols for Satellite Ocean Color Sensor Validation, Revision 

4, Volume III: Radiometric Measurements and Data Analysis Protocols. NASA TM 2003-

211621/Rev.4-Vol.III, Mueller, J.L., G. Fargion and C.R. McClain (Eds.), NASA Goddard Space 

Flight Center, Greenbelt, Maryland, 78pp. 

Mueller, J.L., et al., 2003d: Ocean Optics Protocols for Satellite Ocean Color Sensor Validation, Revision 

4, Volume V: Biogeochemical and Bio-optical Measurements and Data Analysis Protocols, NASA 

TM 2003-211621/Rev.4-Vol.V, Mueller, J.L., G. Fargion and C.R. McClain (Eds.), NASA Goddard 

Space Flight Center, Greenbelt, Maryland, 25pp. 

Mueller, J.L., et al., 2002e: Ocean Optics Protocols for Satellite Ocean Color Sensor Validation, Revision 

4, Volume I: Special Topics in Ocean Optics Protocols and Appendices. NASA TM 2003-

211621/Rev.4-Vol.VI, Mueller, J.L., G. Fargion and C.R. McClain (Eds.), NASA Goddard Space 

Flight Center, Greenbelt, Maryland, 139pp. 

Pegau, S., et al., 2003: Ocean Optics Protocols for Satellite Ocean Color Sensor Validation, Revision 4, 

Volume IV: Inherent Optical Properties: Instruments, Characterizations, Field Measurements and Data 

Analysis Protocols. NASA TM 2003-211621/Rev.4-Vol.VI, Mueller, J.L., G. Fargion and C.R.

McClain (Eds.), NASA Goddard Space Flight Center, Greenbelt, Maryland, 76pp. 

146 
SIMBIOS Project Annual Report

Chapter 14 

Bermuda Bio Optics Project 

Norm Nelson 

Institute for Computational Earth Science, UCSB, Santa Barbara, California 

14.1 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 timeseries 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 highresolution 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. 

14.2 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 1 contains a list of data products relevant to

SIMBIOS. The BBOP project collects continuous profiles of apparent optical properties (AOPs) in the 

upper 140m and deployments are planned to optimize match-ups with the BATS primary production 

incubations and with SeaWiFS overpasses. In 1999, a free-falling Satlantic profiling radiometer system

(SPMR/SMSR s/n 028) became our primary profiling instrument. The primary optical measurements are 

downwelling vector irradiance and upwelling radiance, Ed(z,t,l) and Lu(z,t,l), respectively. Derived 

products include remote sensing reflectance (Rrs(z,l)) and down- and upwelled attenuation coefficients

(Kd(z,l), Ku(z,l)). 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,l). The instruments are calibrated three times annually at UCSB. Sky radiance measurements were 

collected early in the year, but the Microtops photometer began malfunctioning and it was returned to the 

project in the spring of 2003. 

Bottle samples for fluorometric chlorophyll-a and inherent optical properties (IOPs) are also collected. 

Chlorophyll-a is collected once or twice daily during each cruise. Discrete samples for determining the 

absorption spectra of particulates, aph(z,l) and ad(z,l), and CDOM (ag(z,l)) are collected according to 

Nelson et al (1998). Particulate absorption spectra are determined using the quantitative filter technique 

(Mitchell, 1990) (with a regionally specified beta correction factor) and CDOM absorption according to 

Nelson et al (1998). During the past year, BBOP has participated in 14 BATS cruises. We have submitted 

260 optics profiles, 70 fluorometric Chlorophyll-a profiles, 150 IOP spectra, and 4 Microtops scans for sky

radiance. 

147 
SIMBIOS Project Annual Report

14.3 RESEARCH RESULTS 

Instrument Calibration 

We have continued our documentation of the long-term calibration behavior of our SPMR systems 

using several lamps traceable to our own NIST standard. Channels greater than 500nm continue to show 

no obvious drift, and overall scatter is ~1% and ~2% for irradiance and radiance detectors, respectively.

The coefficients of variation these channels are less than 1%. In general, the radiance channels are more 

stable than the irradiance channels. This suggests that the fault is most likely to be the materials used in the 

cosine collectors. The tendency for the UV and blue channels to vary more than the red is in part due to the 

lower output of calibration lamps at lower wavelengths. The most extreme changes were noted at the 

shortest UV channels in both irradiance heads: up to 50-60% for Ed325 and Es325, and about half that for 

Ed340 and Es340. It appears that this decrease in sensitivity occurred during the first year, and that these 

sensors are now stabile, although the response is still quite variable. The additional year of data now shows 

that two UV radiance and the blue irradiance channels have undergone significant drift toward lower 

sensitivity. The drift in the blue radiance sensors was less apparent, and smaller, about 1-2%, not 

significantly different from variation observed in the green-red channels. All affected data for these 

channels was recalculated using a predicted coefficient rather than a long-term average, and resubmitted. 

Collaborative and Project-Related Activities 

As in past years, the BBOP data set 1) contributed to the validation of satellite data products 

within in the SIMBIOS project and 2) used to make original contributions to scientific literature. In

particular, BBOP is among the clearest ocean sites observed within the SIMBIOS program on a consistent 

basis. This means that BBOP has consistently been the clear water end member in match-up analyses made 

between field and satellite observations. The present version (August 2003) of the SIMBIOS SeaWiFS 

LAC-field observation match-up data set (Bailey et al. 2000) contains 383 independent field and satellite 

observations of water-leaving radiance. Thirty of these observations are from the BBOP project. As 

always these data are available via the world wide web (www.icess.ucsb.edu/bbop.html). 

A primary science result from BBOP has been the observation that the optical properties of open ocean are 

greatly affected by concentrations of colored dissolved organic materials or CDOM (Siegel and Michaels, 

1996; Nelson et al. 1998; Nelson and Siegel, 2002; Siegel et al. 2002). We have shown that CDOM

dominates the optical properties in the blue region of the spectrum using knowledge gained at BBOP and 

available field and satellite data sets (many field observations were supported by the SIMBIOS program). 

Globally, 45% of the non-water absorption at 440 nm is due to CDOM (Siegel et al. 2002). Retrieval

algorithms for CDOM were developed (Maritorena et al. 2002) and used to assess its global distribution 

and variability (Siegel et al. 2002). We have recently shown that the open ocean source of CDOM is the 

heterotrophic cycling of DOM by bacteria (Nelson et al. 2003) where CDOM can be created and destroyed 

by microbes. The interesting result from these microbe batch culture experiments is that the net CDOM 

produced mirrors the “complexity” of the DOM source (i.e., high molecular weight DOM leads to more 

long-lasting CDOM produced). We are presently testing these hypotheses on a new project looking at open 

ocean CDOM cycling supported by NSF. 

Our work on CDOM has lead us to consider its interactions with other photoactive materials. One 

interesting candidate is dimethyl sulfide (DMS). Using existing time series (Dacey et al. 1998) and new 

experimental data, we have calculated the rates that DMS is photolysed by exposure to ultraviolet radiation 

(Toole et al. 2003). Using these results and new observations of microbial DMS (and DMSP) cycling by

bacteria, we have made new and important estimates of the biological cycling of DMS (Toole and Siegel, 

2003). Our supports the notion that DMS is important as an intracellular anti-oxidant removing radicals 

and superoxides within a cell. (Sunda et al. 2002). These results have a critical bearing on how climate 

feedbacks due to marine emissions of DMS might occur (Toole and Siegel, 2003). 

REFERENCES 

Bailey, S.W., C.R. McClain, P.J. Werdell and B.D. Schieber, 2000:Normalized water-leaving radiance and 

chlorophyll a match-up analyses. Chapter 7, In: SeaWiFS Postlaunch Calibration and Validation 

148 
SIMBIOS Project Annual Report

Analyses, Part 2. NASA Tech. Memo. 2000-206892, Vol. 10, S.B. Hooker and E.R. Firestone, Eds., 

NASA Goddard Space Flight Center, Greenbelt MD. 

Dacey, J.W.H., F.A. Howse, A.F. Michaels, and S.G. Wakeham. 1998: Temporal variability of 

dimethylsulfide and dimethylsulfoniopropionate in the Sargasso Sea. Deep-Sea Research 45: 2085-

2104. 

Nelson, N.B., and D.A. Siegel, 2002: Chromophoric DOM in the Open Ocean. In: Biogeochemistry of

Marine Dissolved Organic Matter, D.A. Hansell and C.A. Carlson, eds. p. 547-578, Academic Press, 

San Diego, CA. 

Maritorena, S., D.A. Siegel and A.R. Peterson, 2002: Optimal tuning of a semi-analytical model for global

applications. Applied Optics-LP, 41, 2705-2714. 

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. 

Mueller, J. L. and R. W Austin. 1995. Ocean optics protocols for SeaWiFS Validation, revision 1. NASA 

Tech. Memo. 104566, v. 25: (Hooker, S, E. Firestone and J. Acker, eds.), 67 pp. 

O'Brien, M.C., D.W Menzies, D.A. Siegel, and R.C. Smith, 2000: Long-Term Calibration History of 

Several Marine Environmental Radiometers (MERs). NASA Tech. Memo, SeaWiFS Postlaunch 

Calibration and Validation Analyses, Vol. 11, Part 3, Chap. 4. 

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., and A.F. Michaels, 1996: Quantification of non-algal light attenuation in the Sargasso Sea:

Implications for biogeochemistry and remote sensing. Deep-Sea Research II, 43, 321-345. 

Siegel, D.A., S. Maritorena, N. B. Nelson, D.A. Hansell and M. Lorenzi-Kayser, 2002: Global ocean 

distribution and dynamics of colored dissolved and detrital organic materials. Journal of Geophysical

Research, 107, 3228, DOI: 10.1029/2001JC000965. 

Sunda,W., D.J. Kieber, R. P. Kiene & S. Huntsman, 2002, An antioxidant function for DMSP and DMS in

marine algae. Nature, 418, 317 – 320. 

Toole, D.A., and D.A. Siegel, 2003: Light and the open ocean cycling of dimethyl sulfide (DMS). 

Submitted to Nature. 

Toole, D.A., D.J. Kieber, R.P. Kiene, D.A. Siegel and N.B. Nelson, 2003: Photolysis and the 

dimethylsulfide (DMS) summer paradox in the Sargasso Sea. Limnology and Oceanography, 48, 1088-

1100. 

149 
SIMBIOS Project Annual Report

Table 14.1: Partial List of Measurements Made by BBOP & BATS 

BBOP 

Direct Measurements: 

Ed(z,l) Downwelling vector irradiance (325, 340, 380, 412, 443, 488, 510, 555, 565, 665 & 683 

nm) 

Ed(0+,l) Incident irradiance (325, 340, 380, 412, 443, 488, 510, 555, 565, 665 & 683 nm) 

Lu(z,l) Upwelling radiance (325, 340, 380, 412, 443, 488, 510, 555, 565, 665 & 683 nm) 

chl-fl(z) Chlorophyll fluorescence with a WetStar fluorometer 

T(z) & S(z) Temperature and conductivity with Ocean Sensors probes (calibrations by Satlantic) 

atp(l) Particulate absorption spectrum by QFT 

ad(l) Detrital particle absorption spectrum by MeOH extraction

ays(l) Colored dissolved absorption spectrum 

chl-a(z) Discrete chlorophyll a determinations via Turner fluorometry 

Primary Derived Products:

LwN(l) Normalized water leaving radiance (325, 340, 380, 412, 443, 488, 510, 555, 565, 665 & 

683 nm) 

RRS(0-,l) In-water remote sensing reflectance (325, 340, 380, 412, 443, 488, 510, 555, 565, 665 & 

683 nm) 

Kd(z,l) Attenuation coefficient for Ed(z,l) (325, 340, 380, 412, 443, 488, 510, 555, 565, 665 & 

683 nm) 

Kl(z,l) Attenuation coefficient for Lu(z,l) (325, 340, 380, 412, 443, 488, 510, 555, 565, 665 & 

683 nm) 

aph(l) Phytoplankton absorption spectrum (= ap(l) - adet(l)) 

<PAR(z)> Daily mean photosynthetically available radiation at depths of the in situ C14 incubations 

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 

Validation spatial cruises (5 days, 4cruises/year) Deep ocean sediment sinking 

fluxes 

This Research was Supported by 

the NASA Contract # 00200 

Publications 

Siegel, D.A., S. Maritorena, N. B. Nelson, D.A. Hansell and M. Lorenzi-Kayser, 2002: Global ocean 

distribution and dynamics of colored dissolved and detrital organic materials. Journal of Geophysical

Research, 107, 3228, DOI: 10.1029/2001JC000965. 

Chomko, R.M., H.R. Gordon, S. Maritorena and D.A. Siegel, 2003: Simultaneous determination of oceanic 

and atmospheric parameters for ocean color imagery by spectral optimization: a validation. Remote 

Sensing of the Environment, 84, 208-220. 

150 
SIMBIOS Project Annual Report

Westberry T.K., and D.A. Siegel, 2003: Phytoplankton natural fluorescence in the Sargasso Sea: Prediction 

of primary production and eddy induced nutrient fluxes. Deep-Sea Research, Part I, 50, 417-434. 

Toole, D.A., D.J. Kieber, R.P. Kiene, D.A. Siegel and N.B. Nelson, 2003: Photolysis and the 

dimethylsulfide (DMS) summer paradox in the Sargasso Sea. Limnology and Oceanography, 48, 1088-

1100. 

Nelson, N.B., C.A. Carlson, and D.A. Steinberg. 2003, Production of chromophoric dissolved organic 

matter by Sargasso Sea microbes. In press, Marine Chemistry. 

Nelson, N.B., D.A. Siegel, and J.A. Yoder, 2003, The spring bloom in the northwestern Sargasso Sea:

Spatial extent and relationship with winter mixing. Accepted for publication in Deep-Sea Research, 

Part II.

Submitted 

Behrenfeld, M.J., E. Boss, D.A. Siegel and D.M. Shea, 2003, Global remote sensing of phytoplankton 

physiology. Submitted to Science. 

Toole, D.A., and D.A. Siegel, 2003: Light and the open ocean cycling of dimethyl sulfide (DMS). 

Submitted to Nature. 

 

Presentations 

Toole, D.A., R.P. Kiene, and D. Slezak, D.J. Kieber, and D.A. Siegel, Rates and Quantum Yields of 

Dimethylsulfide Photo-oxidation in the Western Atlantic Ocean, 2002 3rd international symposium on 

biological and environmental chemistry of DMS(P) and related compounds, September 2002, 

Rimouski, Quebec, Canada, poster presentation. 

Maritorena, S., D.A. Siegel and D.B. Court, 2002: Ocean color data merging using a semi-analytical model. 

Poster presented at the Ocean Optics XIII meeting, Sante Fe, NM, November 2002. 

Westberry, T., D.A. Siegel and A. Subramaniam, 2002: New techniques for the remote sensing of 

Trichodesmium. Poster presented at the 2002 AGU Fall Meeting, San Francisco, CA, December 2002. 

Toole, D.A., D.A. Siegel, D. Slezak, R.P. Kiene, N.B. Nelson and J.W. Dacey, 2002: A light driven upperocean dimethylsulfide (DMS) biogeochemical cycling model for the Sargasso Sea. Poster presented at 

the 2002 AGU Fall Meeting, San Francisco, CA, December 2002. 

Siegel, D.A., J.C. Ohlmann, S.C. Doney, C.R. McClain, 2002: Variability of ocean radiant heating in the 

Eastern Tropical Pacific. Poster presented at the 2002 AGU Fall Meeting, San Francisco, CA, 

December 2002. 

Siegel, D.A., 2003: Sverdrup's critical depth hypothesis and the North Atlantic spring bloom. Presentation 

made for the International JGOFS North Atlantic Synthesis Group, Toulouse, FR., January 2003. 

Klamberg, J.L., N.B. Nelson and D.A. Siegel, 2003: One-dimensional modeling of seasonal colored 

dissolved organic matter dynamics in the Sargasso Sea. Poster presented at the 2003 ASLO Aquatic

Sciences meeting, Salt Lake City, UT, February 2003. 

Siegel, D.A., N.B. Nelson, S. Maritorena, C.A. Carlson, A.F. Michaels, D.A. Hansell and D.K. Steinberg, 

2003: Distribution, dynamics and biogeochemical implications of colored dissolved organic materials 

in the open ocean. Poster presented at the NASA Ocean Color Science Team Meeting, Miami FL, 

April 2003. 

151 
SIMBIOS Project Annual Report

Maritorena, S., D.A. Siegel and D.B. Court, 2003: Ocean color data merging using a semi-analytical model. 

Poster presented at the NASA Ocean Color Science Team Meeting, Miami FL, April 2003. 

Siegel, D.A., 2003: Reenvisioning the Ocean: The View from Space A RESPONSE, Invited rebuttal 

presented at the JGOFS Open Science Conference, Washington DC, May 2003. 

Siegel, D.A., N.B. Nelson, S. Maritorena, C.A. Carlson, A.F. Michaels, D.A. Hansell and D.K. Steinberg, 

2003: Distribution, dynamics and biogeochemical implications of colored dissolved organic materials 

in the open ocean: discoveries made during the JGOFS era. Poster presented at the JGOFS Open 

Science Conference, Washington DC, May 2003. 

152 
SIMBIOS Project Annual Report

Chapter 15 

Plumes and Blooms: Modeling the Case II Waters of the 

Santa Barbara Channel 

D.A. Siegel, S. Maritorena and N.B. Nelson 

Institute for Computational Earth System Science, University of California, Santa 

Barbara, California 

15.1 INTRODUCTION 

The goal of the Plumes and Blooms (PnB) project is to develop, validate and apply to imagery state-ofthe-art ocean color algorithms for quantifying sediment plumes and phytoplankton blooms for the Case II 

environment of the Santa Barbara Channel. We conduct monthly to twice-monthly transect observations 

across the Santa Barbara Channel to develop an algorithm development and product validation data set.

The PnB field program started in the summer of 1996. At each of the 7 PnB stations, a complete 

verification bio-geo-optical data set is collected. Included are redundant measures of apparent optical

properties (remote sensing reflectance and diffuse attenuation spectra), as well as in situ profiles of spectral

absorption, beam attenuation and backscattering coefficients. Water samples are analyzed for component in

vivo absorption spectra, fluorometric chlorophyll, phytoplankton pigment (by the SDSU CHORS 

laboratory), and inorganic nutrient concentrations (Table 15.1). A primary goal is to use the PnB field data

set to objectively tune semi-analytical models of ocean color for this site and apply them using available 

satellite imagery (SeaWiFS and MODIS). In support of this goal, we have also been addressing SeaWiFS 

ocean color and AVHRR SST imagery (Otero and Siegel, 2003). We also are using the PnB data set to 

address time/space variability of water masses in the Santa Barbara Channel and its relationship to the 

1997/1998 El Niño. However, the comparison between PnB field observations and satellite estimates of 

primary products has been disappointing. We find that field estimates of water-leaving radiance, LwN(λ), 

correspond poorly to satellite estimates for both SeaWiFS and MODIS local area coverage imagery. We

believe this is due to poor atmospheric correction due to complex mixtures of aerosol types found in these 

near-coastal regions. Last, we remain active in outreach activities. 

15.2 RESEARCH ACTIVITIES 

We have conducted 12 one-day cruises since November 2002 and anticipate conducting two more 

before the end of the contract. To date combination, we have completed 70 stations (up to 14 more by the 

end of the project) comprised of CTD/rosette casts, spectroradiometer profiles and in situ IOP 

determinations (AC9 & hydroscat). All available processed data has been submitted to the SeaBASS 

system and we will complete this task by November 1, 2003. 

In January of 2003, the Channel Islands National Marine Sanctuary (CINMS) received delivery of its 

new research vessel, the R/V Shearwater. This finally has given us consistent platform to work from since 

the loss of the R/V Ballena in 2001. However, it hasn’t come easy. We have experienced several aborted 

cruises early this year due to “new ship issues”. This has been frustrating but we are now finally over the 

top. We have worked with CINMS and NOAA/NOS staff throughout this process and I think we finally

have a stable field observation configuration for PnB cruises. We have continued our acquisition and 

analysis of satellite ocean color (SeaWiFS) and thermal imagery (AVHRR) from the UCSB HRPT ground 

station (HUSC). The UCSB ground station presently supported by SIMBIOS and NOAA/NESDIS. 

153 
SIMBIOS Project Annual Report

The PnB project has an extensive education and outreach component. Typically, two undergraduate 

students volunteer on each one day cruise and the PnB data is presently being used in the theses and 

dissertations of three graduate students at UCSB. We also participated in the California Fish & Game

workshops on monitoring the status of no-take marine protected areas around the CINMS. Satellite and 

field data supported by SIMBIOS are being used in this public process. 

Table 15.1: Plumes and blooms measurements and data products

Direct Measurements: 

Ed(z,λ) Downwelling vector irradiance (325, 340, 380, 412, 443, 490, 510, 555, 565, 665 & 683 nm)

Ed(0+,λ) Incident irradiance (325, 340, 380, 412, 443, 490, 510, 555, 565, 665, 683 & 350-1050 nm)

Lu(z,λ) Upwelling radiance (325, 340, 380, 412, 443, 490, 510, 555, 565, 665 & 683 nm)

a(z,λ) In situ absorption spectrum using WetLabs AC-9 (410,440,490,520,565,650,676 & 715 nm)

c(z,λ) In situ beam attenuation spectrum (same as above) 

bb(z,λ) In situ backscattering spectrum - HOBI Hydroscat (442,470,510,532, 590 & 671 nm)

T(z) & S(z) SeaBird temperature and conductivity probes 

ap(zo,λ) In vivo particulate absorption spectrum by Mitchell (1990) 

adet(zo,λ) In vivo detrital particle absorption spectrum by MeOH extraction 

ag(zo,λ) In vivo colored dissolved absorption spectrum

chl-a(zo) Discrete chlorophyll a determinations by Turner fluorometry 

pigs(zo) Discrete phytoplankton pigment sample to be run by HPLC (SDSU CHORS analysis) 

nuts(zo) Discrete inorganic nutrient concentrations (NO3, SiO4, PO4, NO2) 

Lsat(x,y,λ) SeaWiFS and AVHRR imagery from the HUSC ground station 

Primary Derived Products: 

Rrs(0-,λ) In-water remote sensing reflectance from profiling radiometry (see above) 

LwN(λ) Normalized water leaving radiance calculated from RRS(0+,λ) and RRS(0-

,λ) 

aph(zo,λ) In vivo phytoplankton absorption spectrum (= ap(zo,λ) - adet(zo,λ)) 

Kd(z,λ) Attenuation coefficient for Ed(z,λ) from profiling radiometry (also KL(z,λ)) 

b(z,λ) In situ total scattering spectrum (= c(z,λ) - a(z,λ)) 

Chl(x,y), etc. Processed SeaWiFS and AVHRR imagery

15.3RESEARCH RESULTS 

Academic research has gone along two major fronts; 1) the validation and development of satellite data 

products using the PnB data set (Siegel et al. 2002; 2003a; Otero and Siegel, 2003; Warrick et al. 2003a) 

and 2) the analyses of field and satellite data to assessment of the time/space variability in the Santa 

Barbara Channel (Shipe et al. 2002; Siegel et al. 2003b; Otero and Siegel, 2003; Warrick et al. 2003b;

2003c). We have used the PnB data set extensively in validation and development of satellite data 

products. This includes work related to development and application of global algorithms (Maritorena et

al. 2002; Siegel et al. 2002; 2003c) and at the local scale (Warrick et al. 2003a). In particular, we have 

developed a simple mixing model to estimate suspended sediment concentrations for the Santa Clara River 

outflow plume (Warrick et al. 2003a). Jon Warrick, a recent PhD graduate from our group, has applied this 

algorithm to SeaWiFS imagery to examine the fate of river-borne sediment concentrations as they enter the 

Santa Barbara Channel (Mertes and Warrick, 2001; Warrick et al. 2003b; 2003c). We are still in progress in 

developing a regional semi-analytical model for this site (following Maritorena et al. 2002) and a new 

graduate student researcher (Tihomir Kostadinov) has taken this on as his Masters project. 

Unfortunately, the hard part about building detailed predictive models of Case II ocean color variability 

is that matchups between satellite and in situ observations of water-leaving radiance, LwN(λ), are often poor 

and this suggests that significant improvements are required in the assumptions used to drive the 

atmospheric correction procedure (Siegel et al. 2003a). For example, figure 1 shows that both MODIS and 

SeaWiFS often, though not always, dramatically underestimate LwN(λ), particularly for the violet to blue 

wavebands. It is premature to make more definitive statements of the comparisons between the two

satellite sensors due to the comparatively fewer number of matchup observations found using MODIS (29 

154 
SIMBIOS Project Annual Report

as compared with as many as 314 found for SeaWiFS were available when this work was performed). 

However, these discrepancies are not due to problems with the field data as the satellite retrieved LwN(412) 

and LwN(443) values shown are much lower than typical values expected (Siegel et al. 2003a). The fact

that the retrievals sometimes work and sometimes do not suggests that issues with the satellite’s 

radiometric calibration are also not at fault either. That is, there is no obvious trending the discrepancy with

the field observed LwN(412) and LwN(443) values as one would expect with for a static calibration error 

arising from the difference between two large numbers (measured and modeled top of the atmosphere 

radiance). Both sensors do a fair job (though clearly not spectacular) predicting LwN(555) and chlorophyll

concentrations (r2 = 0.50 [LwN(555)] & 0.56 [Chl] for SeaWiFS and r2 = 0.68 & 0.62 for MODIS for the 

same quantities). This reasonable success gives some confidence in the scientific application of these data 

to near-coastal regions (see also Otero and Siegel, 2003). 

Figure 15.1: Matchup comparisons of satellite vs. field observations for normalized water-leaving radiance 

and chlorophyll a concentration for both SeaWiFS (+) and MODIS (*). Only MODIS data from Terra are 

shown and all data pass the quality zero flag. The OC3M chlorophyll estimate for MODIS is shown as 

there are 29 retrievals with this algorithm vs. 13 available from the MODIS semi-analytical algorithm

(failures in the chl_a3 MODIS data product are quality flagged). 

This simple matchup analysis suggests that substantial improvements are needed in the procedures 

used to correct satellite radiance for the complicating effects of the atmosphere. This may be true for 

global ocean retrievals, but it will be particularly acute for the coastal zone where state-of-the-art ocean 

color algorithms cannot be used due to the poor performance of present atmospheric correction procedures. 

We believe these difficulties are algorithmic (due to processing procedures and codes) and NOT to

inadequacies in the field observations or in the radiometric calibration of satellite observations. Present 

techniques used by both SeaWiFS and MODIS to correct for the effects of the atmosphere are based on the 

initial framework of Gordon and Wang [1994] (see also Gordon and Voss, 1999). The Gordon and Wang 

(1994) algorithm uses spectral bands in the near infrared (NIR) to assess the aerosol’s contribution to the 

155 
SIMBIOS Project Annual Report

reflectance in the visible. This approach is applicable only for a non- or weakly-absorbing aerosol

(Gordon, 1997). When the aerosol is strongly absorbing, associated with soot, pollution, biomass burning, 

pollen or dust aerosols, the NIR reflectance provides no clue to the path radiance that an atmospheric 

correction algorithm must correct for. This means that the two-step atmospheric correction process 

implemented in the retrieval of MODIS ocean color products will fail in the presence of strongly absorbing 

aerosols. The advantage of the spectral mixing approach of Warrick et al. [2003a] is that anomalies in the 

atmospheric path radiance due to complex aerosol distributions can be treated in an ad hoc manner enabling 

the sediment signal to be quantified. Semi-analytical methods (such as Maritorena et al. 2002) will NOT 

perform well under these conditions. We have ideas (in pending proposals!!) to develop novel correction 

procedures that simultaneously account for complex atmosphere and ocean interactions following 

approaches outline by Chomko et al. [2003]. 

We have continued our analyses of oceanographic processes using the six-year PnB observational

record. Of continuing interest is our work assessing the sources and modes of ocean color variability 

within the Santa Barbara Channel (Toole and Siegel, 2001; Siegel et al. 2003b). The characterization of the 

substances, processes, and mechanisms that regulate coastal ocean color variability is crucial for the 

application of ocean color imagery to the management of marine resources. Using an empirical orthogonal 

function (EOF) analysis, we find that nearly two-thirds of the observed variability in remote sensing

reflectance is contained in a backscattering mode. Phytoplankton absorption makes a much smaller 

contribution to the observed variance in the water-leaving radiance spectrum. Hence, particulate 

backscattering associated with suspended sediment concentrations is the dominant driver of ocean color 

variability for this environment. However, sediment plumes appear to play a much smaller role on

biological processes. An empirical partitioning of physical, biological and chemical oceanographic 

parameters suggests that physical oceanographic processes (i.e., upwelling and horizontal advection) have 

the dominant role in determining phytoplankton pigment biomass for this region. One goal is to use these 

spectral EOF modes with satellite data to map out where and when blooms occur. Using EOF analysis of

the space/time patterns of satellite ocean color imagery, we find a similar partitioning (Otero and Siegel, 

2003). This work attempts to differentiate between the role of local (wind-driven upwelling and seasonal 

heating) vs. non-local (i.e., advection and ENSO) processes in regulating biological distributions in the 

Santa Barbara Channel (Otero and Siegel, 2003). A similar analysis is looking at the water quality 

measurements (nutrients, chlorophyll and optical properties, etc.) to address how physical processes are 

regulating optical variability in the Santa Barbara Channel (Siegel et al. 2003b). Further, a new graduate 

student supported by the Santa Barbara Coastal-LTER program, Clarissa Anderson, is working with the 

HPLC pigment data set to look at phytoplankton succession processes and their control by physical 

oceanographic proceses (Anderson et al. 2003). 

On a last technical note, throughout the PnB time series we have noticed that our observations of the 

specific absorption of phytoplankton at 676 nm [a*ph(676)] often exceed the theoretical maximum of 

0.0206 m2.mg-1. We now know that the reason for this overestimate was the use of previously published 

pathlength amplification factors (β) suggested by Mitchell (1990) from experiments using mixed 

phytoplankton cultures. To assess this source of bias, we conducted a set of experiments to test the 

relationship between the optical density in suspension (ODs) and the optical density of the same sample on 

a GF/F filter (ODf) of natural samples from PnB waters enabling us to determine our own β factor for our 

own spectrophotometer (Guillocheau, 2003). We conducted three independent determinations of the β

factor which were consistent with each other (within a factor of 10%). However, the composite PnB β

factor is a factor of 1.7 times lower than the Mitchell (1990) coefficients. Procedures used are documented 

in Guillocheau [2003]. We subsequently corrected all of our filter-pad absorption data and have submitted 

these to the SeaBASS data set. 

REFERENCES 

Anderson, C.R., D.A. Siegel, M.A. Brzezinski, N. Guillocheau and D.A. Toole, 2003: A time series 

assessment of phytoplankton community structure in the Santa Barbara Channel. Poster presented at

the 2003 ASLO Aquatic Sciences meeting, Salt Lake City, UT, February 2003. 

156 
SIMBIOS Project Annual Report

Chomko, R.M., H.R. Gordon, S. Maritorena and D.A. Siegel, 2003: Simultaneous determination of oceanic 

and atmospheric parameters for ocean color imagery by spectral optimization: a validation. Remote 

Sensing of the Environment, 84, 208-220. 

Guillocheau, N., 2003: Beta-Correction Experiment Report. ICESS Internal document, UCSB, April 2003, 

www.icess.ucsb.edu/~davey/MyPapers/beta_corr_report2.doc.

Mertes, L.A.K., and J.A. Warrick, 2001. Measuring flood output from 110 coastal watersheds in California 

with field measurements and SeaWiFS. Geology, 29, 659-662. 

Otero, M.P., and D.A. Siegel, 2003: Spatial and temporal characteristics of sediment plumes and 

phytoplankton blooms in the Santa Barbara Channel. Accepted for publication in Deep-Sea Research, 

Part II. 

Shipe, R.F., U. Passow, M.A. Brzezinski, D.A. Siegel and A.L. Alldredge, 2002: Effects of the 1997-98 El 

Nino on seasonal variations in suspended and sinking particles in the Santa Barbara Basin. Progress in

Oceanography, 54, 105-127. 

Siegel, D.A., S. Maritorena, N. B. Nelson, D.A. Hansell and M. Lorenzi-Kayser, 2002: Global ocean 

distribution and dynamics of colored dissolved and detrital organic materials. Journal of Geophysical

Research, 107, 3228, DOI: 10.1029/2001JC000965. 

Siegel, D.A., E. Fields O. Polyakov, N. Guillocheau and D.W. Menzies, 2003a, Plumes and Blooms FieldSatellite Matchup Analyses. ICESS internal report. Available at 

www.icess.ucsb.edu/~davey/MyPapers/PnB_Matchups_March2003.doc. 

Siegel, D.A., L. Washburn, J. Warrick, D.A. Toole, J. Sorensen, O. Polyakov, M.P. Otero, M.J. Neumann, 

L.A.K. Mertes, N. Guillocheau, D. Fernamburg and M.A. Brzezinski, 2003b: A time-series assessment 

of sediment plumes and phytoplankton blooms in the Santa Barbara Channel, California, To be 

submitted to Continental-Shelf Research. 

Toole, D.A., and D.A. Siegel, 2001: Modes and mechanisms of ocean color variability in the Santa Barbara 

Channel. Journal of Geophysical Research, 106, 26,985-27,000. 

Warrick, J.A., L.A.K. Mertes, D.A. Siegel and C. MacKenzie, 2003a: Estimating suspended sediment

concentrations in turbid coastal waters with SeaWiFS. In press International Journal of Remote

Sensing. 

Warrick J.A., L.A.K. Mertes, L. Washburn, and D.A. Siegel, 2003b: A conceptual model for river water 

and sediment dispersal in the Santa Barbara Channel, California. Submitted to Continental Shelf 

Research. 

Warrick J.A., L.A.K. Mertes, L. Washburn, and D.A. Siegel, 2003c: Dispersal forcing of southern 

California river plumes, based on field and remote sensing observations. Submitted to Geo-Marine 

Letters. 

Warrick J.A., L. Washburn, M.A. Brzezinski, and D.A. Siegel, 2003d: Nutrient contributions to the Santa 

Barbara Channel, California, from the ephemeral Santa Clara River. Submitted to Estuarine, Coastal 

and Shelf Research. 

157 
SIMBIOS Project Annual Report

This Research was Supported by 

 the NASA Contract # 00201

Publications (* denotes student papers)

Behrenfeld, M.J., E. Boss, D.A. Siegel and D.M. Shea, 2003: Global remote sensing of phytoplankton 

physiology. Submitted to Science. 

Chomko, R.M., H.R. Gordon, S. Maritorena and D.A. Siegel, 2003: Simultaneous determination of oceanic 

and atmospheric parameters for ocean color imagery by spectral optimization: a validation. Remote 

Sensing of the Environment, 84, 208-220. 

Guillocheau, N., 2003: Beta-Correction Experiment Report. ICESS Internal document, UCSB, April 2003, 

www.icess.ucsb.edu/~davey/MyPapers/beta_corr_report2.doc.

McPhee-Shaw, E., D.A. Siegel, L. Washburn, M.A. Brzezinski, and J.A. Jones, 2003, Mechanisms for 

nutrient delivery to the inner shelf: Observations from the Santa Barbara Channel. For Limnology and 

Oceanography. 

Otero, M.P., and D.A. Siegel, 2003: Spatial and temporal characteristics of sediment plumes and 

phytoplankton blooms in the Santa Barbara Channel. Accepted for publication in Deep-Sea Research, 

Part II. (*) 

Shipe, R.F., U. Passow, M.A. Brzezinski, D.A. Siegel and A.L. Alldredge, 2002: Effects of the 1997-98 El 

Nino on seasonal variations in suspended and sinking particles in the Santa Barbara Basin. Progress in

Oceanography, 54, 105-127. (*) 

Siegel, D.A., .P. Kinlan, B. Gaylord and S.D. Gaines, 2003: Lagrangian descriptions of marine larval

dispersion. Marine Ecology Progress Series, 260, 83-96. 

Siegel, D.A., S. Maritorena, N. B. Nelson, D.A. Hansell and M. Lorenzi-Kayser, 2002: Global ocean 

distribution and dynamics of colored dissolved and detrital organic materials. Journal of Geophysical

Research, 107, 3228, DOI: 10.1029/2001JC000965. 

Siegel, D.A., L. Washburn, J. Warrick, D.A. Toole, J. Sorensen, O. Polyakov, M.P. Otero, M.J. Neumann, 

L.A.K. Mertes, N. Guillocheau, D. Fernamburg and M.A. Brzezinski, 2003: A time-series assessment 

of sediment plumes and phytoplankton blooms in the Santa Barbara Channel, California, To be 

submitted to Continental-Shelf Research. 

Siegel, D.A., E. Fields O. Polyakov, N. Guillocheau and D.W. Menzies, 2003, Plumes and Blooms FieldSatellite Matchup Analyses. ICESS internal report. Available at 

www.icess.ucsb.edu/~davey/MyPapers/PnB_Matchups_March2003.doc. 

Siegel, D.A., S. Maritorena, N.B. Nelson, 2003, Independence and interdependencies of ocean optical 

properties viewed using SeaWiFS. For Journal of Geophysical Research. 

Warrick, J.A., L.A.K. Mertes, D.A. Siegel and C. MacKenzie, 2003: Estimating suspended sediment

concentrations in turbid coastal waters with SeaWiFS. In press International Journal of Remote

Sensing. (*) 

Warrick J.A., L.A.K. Mertes, L. Washburn, and D.A. Siegel, 2003: A conceptual model for river water and 

sediment dispersal in the Santa Barbara Channel, California. Submitted to Continental Shelf Research. 

(*) 

158 
SIMBIOS Project Annual Report

Warrick J.A., L.A.K. Mertes, L. Washburn, and D.A. Siegel, 2003: Dispersal forcing of southern 

California river plumes, based on field and remote sensing observations. Submitted to Geo-Marine 

Letters. 

Warrick J.A., L. Washburn, M.A. Brzezinski, and D.A. Siegel, 2003: Nutrient contributions to the Santa 

Barbara Channel, California, from the ephemeral Santa Clara River. Submitted to Estuarine, Coastal 

and Shelf Research. (*) 

Presentations 

Anderson, C.R., D.A. Siegel, M.A. Brzezinski, N. Guillocheau and D.A. Toole, 2003: A time series 

assessment of phytoplankton community structure in the Santa Barbara Channel. Poster presented at

the 2003 ASLO Aquatic Sciences meeting, Salt Lake City, UT, February 2003. 

McPhee-Shaw, E.E., L. Washburn, D.A. Siegel, and M.A. Brzezinski, 2002: The Santa Barbara Channel 

LTER (Long-term ecological research) study. Oceanographic time-series data from nearshore stations, 

2001, with implications for nutrient delivery to kelp reefs. Talk given at EPOC (Eastern Pacific Ocean 

Conference) meeting, Mt Hood, OR, September 25028, 2002. 

McPhee-Shaw, E.E., M.A. Brzezinksi, D.A. Siegel, and L. Washburn, 2002: The Santa Barbara Channel 

LTER Oceanographic data from near-shore stations, 2001-2002, with implications for nutrient delivery 

to kelp reefs. Poster presented at the California and World Ocean ’02 Conference, Santa Barbara, CA, 

October 27-30, 2002. 

Ow, L., L. Washburn, D.A. Siegel, and E.E. McPhee-Shaw, 2003: Moored observations of biological and 

physical variability near kelp reefs in the Santa Barbara Channel. Poster presented at the 2003 ASLO 

Aquatic Sciences meeting, Salt Lake City, UT, February 2003. 

Senyk, N., and D.A. Siegel, 2002: Using remotely sensed data to describe spatial and temporal habitat

distributions of the giant kelp, Macrocystis pyrifera. Poster presented at the California and World 

Ocean '02 Conference, Santa Barbara, CA, October 27-30, 2002. 

Siegel, D.A., 2002: Flow, fish and fishing or the modeling of marine protected area efficiency. Colloquium

presented at the UCSB Geography Department, November, 2002. 

Siegel, D.A., N.B. Nelson, S. Maritorena, C.A. Carlson, A.F. Michaels, D.A. Hansell and D.K. Steinberg, 

2003: Distribution, dynamics and biogeochemical implications of colored dissolved organic materials 

in the open ocean: discoveries made during the JGOFS era. Poster presented at the JGOFS Open 

Science Conference, Washington DC, May 2003. 

Siegel, D.A., O. Polyakov, E. Fields and N. Guillocheau, 2003: Comparison of SeaWiFS and MODIS 

water leaving radiance spectra with in situ observations from the Santa Barbara Channel: Role of 

aerosol absorption, Poster presented at the NASA Ocean Color Science Team Meeting, Miami FL, 

April 2003. 

 

159 
SIMBIOS Project Annual Report

Chapter 16 

Algorithms for Processing and Analysis of Ocean Color

Satellite Data for Coastal Case 2 Waters 

Richard P. Stumpf 

NOAA National Ocean Service, Silver Spring, Maryland 

Robert A. Arnone and Richard W. Gould, Jr. 

Naval Research Laboratory (NRL), Stennis Space Center, Mississippi 

Varis Ransibrahmanakul 

Technology Planning and Management Corporation, Silver Spring, Maryland 

Patricia A. Tester 

NOAA National Ocean Service, Beaufort, North Carolina 

16.1 INTRODUCTION 

 SeaWiFS has the ability to enhance our understanding of many oceanographic processes. However, its 

utility in the coastal zone has been limited by valid bio-optical algorithms and by the determination of

accurate water reflectances, particularly in the blue bands (412-490 nm), which have a significant impact 

on the effectiveness of all bio-optical algorithms. We have made advances in three areas: algorithm

development (Table 16.1), field data collection, and data applications. 

Table 16.1: Issues and advances to atmospheric correction. 

Issues Advances

1. The previous atmospheric 

correction poorly estimates water 

leaving radiance in the near infrared 

(NIR) when detritus and chlorophyll

don’t co-vary. 

1. We offer an NIR correction, which draws on coupling 

the atmospheric and oceanic models (Stumpf et al., 

2003), to better estimate water reflectance. NASA has 

implemented this correction to process of SeaWIFS data

in July 2002. 

2. The current atmospheric correction 

neglects the effects of absorbing 

aerosols associated such as pollution. 

2. We offer a technique to estimate and remove the 

unresolved absorbing aerosol component in water 

reflectance (Ransibrahmanakul and Stumpf, 2003). 

Data Collection

We have collected data sets of bio-optical properties in turbid Case 2 waters, which are relatively few 

in SeaBASS database. We have collected over 300 stations of AC9, remote sensing reflectance spectra, 

and HPLC (Table 16.2). 

These data sets will provide the basis for improving and evaluating in situ bio-optical and atmospheric 

correction algorithms for coastal waters. The coastal waters with a high sediment load and/or high 

concentrations of colored dissolved organic matter (CDOM), standard processing algorithms typically fail

(negative or erroneous retrievals of water-leaving radiance) due to invalid assumptions related to the 

atmospheric correction. Our efforts have focused on describing the reflectance properties at near-infrared 

(NIR) and blue wavelengths in coastal waters, and utilization these properties to improve the atmospheric 

160 
SIMBIOS Project Annual Report

correction. We have modified and developed new coastal bio-optical algorithms, as well as validated the 

algorithms and atmospheric corrections. 

We are also preparing for MODIS. We have implemented MODCOLOR and MODSST into the NRL 

processing software. This has included modifying the MODIS processing software in cooperation with

NASA and University of Miami to use the same NIR atmospheric correction SEADAS is using.

Table 16.2: Number of stations of various data collected. 

AC9/CTD ASD/remote sensing reflectance HPLC 

352 404 413

Data Applications

In addition to algorithm development, we also utilize the new products to monitor and predict harmful 

algal blooms along the west Florida shelf and develop new products for SeaWiFS and MODIS. 

16.2. RESEARCH ACTIVITIES 

 

Cruises 

We have collected in situ bio-optical data on twenty-five cruises. Measurements in the Northern Gulf 

of Mexico include absorption coefficient, beam attenuation coefficient, scattering coefficient (ac9), remote 

sensing reflectance (above water method), aerosol optical thickness (Microtops), and HPLC pigments

(Table 16.2). A substantial number of these stations were collected during minimum cloud cover and 

matched up with SeaWIFS and MODIS passes. Measurements in North Carolina waters include water 

samples, water profile measurements (YSI), and remote sensing reflectance. The water samples were 

collected to determine chlorophyll, CDOM, filter pad absorption, HPLC, total suspended solids (TSS), and 

nutrients. Measurements along the Gulf coast include remote sensing reflectance from 400-900 nm, spectral

absoprtion and scattering profiles (from an ac9), water samples, and aersol optical depth (MICROTOPS 

sunphotometer). The water samples were collected to determine chlorophyll, CDOM, filtered pad 

absorption, HPLC (through CHORS), total suspended solids (TSS), and nutrients. 

 

The Use of LIDAR to Improve Bio-optical Algorithms (NOAA)

The current chlorophyll algorithm fails in high CDOM areas. We have collected simultaneous 

SeaWIFS, LIDAR, hyperspectral radiance, and water samples from Pamlico Sound to develop a more 

robust Case 2 bio-optical algorithm. Airborne LIDAR have been used to determine synoptic chlorophyll-a

and CDOM in coastal North Carolina. Pat Tester has started a collaborative research project with Bob 

Swift at NASA Wallops Island. She has contracted for six more over flight windows to use LIDAR during 

the spring and fall from 2003 through 2005. In addition to the utility of CDOM (and organic C), CDOM 

signals may serve as mimics for salinity or nutrients pulses after runoff events. 

 

16.3. RESEARCH RESULTS 

 NIR atmospheric correction implemented to latest SEADAS (NOAA & NRL)

We submitted NIR atmospheric correction code and manuscript to Sean Bailey in March and

September 2002, respectively. After Gene Feldman compared the products derived from many atmospheric 

corrections and posted them to SeaWiFS community for review, NASA implemented our NIR-correction 

approach into SEADAS 4.4 (released July 2002). In addition to NASA’s evaluation, we independently 

compared the products derived from different atmospheric corrections against measured data. The 

comparison shows that the proposed NIR reduced both the bias and root-mean square error. Description 

NIR correction may be found in Volume 22 of a NASA technical memo: Algorithm Updates for the Fourth

SeaWiFS Data Processing. 

161 
SIMBIOS Project Annual Report

Table 16.3: Summary of Data Collected 

Experiment Cruise Date

Ac9/CTD 

Etc. 

ASD 

rrs 

HPLC 

Cojet 3–Northern Gulf of 

Mexico

Mopex 5/16/01 26 36 0

Cojet 3–Northern Gulf of 

Mexico

Lgssur 5/16/01 31 31 29

Cojet 3–Northern Gulf of 

Mexico

Ocolor 5/16/01 22 22 25

NGLI Lake Bourne Apr01 Ocolor 04/01 4 3 4 

NGLI Biloxi 11Sep01 Ocolor2 09/01 8 8 8 

Leo2001- East Coast –New 

jersey R/V NorthStar 7/31- 8/02/01 15 28 23 

Cojet 4 – Barrier Islands, 

Mobile Bay 9/2/01 – 9/6/01 17 16 33 

Cojet 5 – Barrier Is., Mobile

Bay Pelican 12/3/01-12/7/01 26 25 26

Cojet 6- Barrier Is., Mobile Bay Pelican 3/5/02-3/6/02 8 9 9 

Cojet 7 – Barrier Island Pelican 5/17/02-5/26/02 29 17 27 

Cojet 7 – Barrier Island Ocolor 5/17/02-5/26/02 24 25 25 

NGLI Biloxi 19Nov01 5/01 5 

ECOHAB- West Florida Shelf ECOHAB 9/25/00-9/29/00 25

NC-04/01- North Carolina Pamlico 4/10/01-4/11/01 8 10 14 

NC-05/01 – North Carolina 5/24/01 4 4 

NC 07/01 North Carolina Pamlico 7/24/01 – 7/26/01 4 11 

NC 08/01 Pamlico/NC shelf 08/1/01-8/2/01 5 

NC-10/01 NC Shelf/Pamlico 10/16/01-10/19/01 8 13

NC-02/02 Pamlico 2/19/02-2/21/02 8 8

CA-03/02 Offshore San Francisco, 

 Gulf of Farallones 

3/2/02-3/4/02 7 20

NC-07/02 Pamlico 7/3/02 2 6

Dolche-Vita Adriatic Sea Feb 03 35 35 

Monterey April 03 59 55 60

Fort Lauderdale Aug 8 – 10, 2003 18 18 0 

Horn Island, near Biloxi, MS Sep 17- 19, 2003 22 22 22 

NC03 4/15/03-4/16/03 11 11

 TOTALS 352 404 413 

Absorbing aerosol correction 

Although the NIR correction improves the accuracy of the derived water reflectance, the correction 

only corrects for the Rayleigh and aerosol scattering but neglects absorption due to aerosols. Thus, the 

current ‘water absorption’ is an overestimate of true water absorption when absorbing aerosols are present. 

The overestimated water absorption begets underestimated reflectance and underestimated chlorophyll.

We have a proposed algorithm to correct for absorbing aerosols and will submit the manuscript to Applied 

Optics. 

Calibration (NOAA)

 

At the AGU meeting in San Francisco, December 2001, Ransibrahmanakul and Stumpf (2001) 

presented a new method for validating calibration gain values in the blue bands. 

162 
SIMBIOS Project Annual Report

Background & Problem 

The current calibration of SeaWiFS involves two steps: correction for temporal changes using lunar 

observations and periodic vicarious calibration of the radiance based on comparison with the Marine 

Optical Buoy (MOBY) sites. The later has potential uncertainty of 0.5 % in the top-of-atmosphere (TOA) 

radiance calibration (Barnes et al. 2000), partly because each band is calibrated independently. In coastal 

areas, the calibration of the 412 nm band is of particular concern owing to the need to correct for absorbing 

aerosols in the atmosphere and the need to monitor and compensate for colored dissolved organic matter 

(CDOM) in the water. Calibration errors of 0.5-1% between bands in the blue (412 nm, 443 nm, 490 nm) 

can introduce significant errors in the retrieved water reflectance (5-10% in case 1 water, and 20-100% in

case 2 water). 

We proposed two methods: one involving examination of case 2 water; another, in all water types. 

Both methods use an inter-band relationship found in the field (and assumed to be intrinsic) to validate the 

spectral shape observed in satellite data. The coastal method examines spectral curvature in the blue 

bands. Satellite data typically shows a convex shape from 412 to 490 nm, while field data shows a 

concave shape. By defining coastal water spectra as those with remote sensing reflectance at 443 nm

greater than remote sensing reflectance at 412 nm, we found that 90% of the 420 field coastal water spectra 

are concave (index < 0). In contrast, 90% of the satellite coastal water spectra are convex (index > 0). 

Using the latest calibration and software, we found the calibration at 412 nm appears to be 

underestimated by about 1%. For validation, we computed the overall bias and root mean square error 

(rms) from 105 pairs of satellite and same-day cloud-free measured remote sensing reflectance in US 

waters. Using the adjusted calibration, the bias at 412 nm is reduced by four folds. Adding a component

to the current protocol to calibrate multiple bands simultaneously may improve the total calibration. 

Remote Sensing Relectance at 670 nm (NOAA)

Remote sensing reflectance at 670 nm is used in the NIR correction. Inaccurate remote sensing 

reflectance at 670 nm would contribute errors in all bands. We have observed large patches of negative 

remote sensing reflectance at 670 nm near the coast in the North Atlantic. In an effort to identify the 

problem, we compared SeaWiFS remote sensing reflectance at 670 nm with two modeled estimates in the 

Sargasso Sea. This area was chosen because of its extremely low chlorophyll characteristics, therefore 

reducing the number of assumptions made in the models. We observed satellite remote sensing reflectance 

at 670 nm to be higher than both modeled estimates. This was unexpected, considering that negative 

remote sensing reflectance has been observed. In conclusion, adjusting the calibration at 670 nm in any

direction is not a global solution, indicating that a local problem, possibly in some of the atmospheric 

correction models, may be producing the negative radiances at 670 nm. 

Validation of Atmospheric Correction and Chlorophyll Algorithms for Processing SeaWiFS data (NOAA)

With at least four atmospheric corrections and chlorophyll algorithms for SeaWiFS available, a user 

may be interested in their performances in different water types. To facilitate this comparison, Zhong Ping 

Lee and Bob Arnone have started an Ocean-Color-Algorithm working Group (OCAG), where the first 

meeting will take place on November 17, 2002, Sata Fe, NM, prior to the Ocean Optics Conference. 

We presented the results comparing atmospheric corrections and five chlorophyll algorithms in

different water types at the 7th International Conference on Remote Sensing for Marine and Coastal

Environments, Miami, May 2002 (Ransibrahamanakul et al., 2002). We have developed an evaluation 

protocol to evaluate the performance of the available atmospheric corrections (including the NOAA/NRL 

developmental atmospheric correction) and chlorophyll algorithms. To date, we have used 159 same-day

field-satellite pairs of remote sensing reflectance spectra to determine the best atmospheric correction

applicable to the entire US coastline. The five atmospheric corrections considered were developed by

Gordon & Wang (1994), Siegel et al. (2000), Gould et al. (1998), Ruddick et al. (2000), and NOAA/NRL 

(Stumpf et al., 2003). We did not include the atmospheric correction developed by Hu et al. (2000) 

because it requires manual interaction and is not appropriate for automated processing. 

163 
SIMBIOS Project Annual Report

Similarly, we have developed protocols and software for evaluating multiple chlorophyll algorithms

for regional application. The analysis involves examination of spatial matches and ranking of the 

procedures. In both the evaluations of atmospheric correction and chlorophyll algorithms, the selection 

procedure was designed to determine an algorithm that works best over a range of water types and 

compensates for distribution biases. The process also allows comparison of the chlorophyll with unique 

optical properties where algorithms may fail. This occurs in Pamlico Sound, North Carolina, during flood 

conditions, and in Atchafalaya Bay, Louisiana, under high flow. We are examining the potential factors. 

MODIS Terra processing (NRL)

We are working with the University of Miami and NASA Goddard for processing MODIS ocean color 

data. We have obtained MODCOLOR and MODSST and implemented on our Linux operating system and 

integrated it into the Automated Processing System (APS) (Navy’s satellite processing software). We have

modified the MODIS software with the NIR atmospheric correction used in SeaWIFS processing and 

implemented coastal algorithms. The output of all the APS is an HDF file format that is directly input into

SEADAS or ENVI. Our efforts in MODIS processing required that we closely coordinate efforts with the 

MODIS Science team and MODIS calibration Science Team. These required updated calibration, level 0 and 

2 processing etc. We have worked with the programmers and scientists at University of Miami (Evans group), 

and Goddard (Esaisas group, and C. Lyons group). They have provided quality data and cooperative efforts 

which have allowed us the ability to do our research. This cooperation is strongly acknowledged. 

We are generating the standard NASA products (Chlorophyll, absorption and scattering) in addition to 

bio-optical navy algorithms (over 50 products) in both MODIS (TERRA) and SeaWIFS. We are comparing 

the differences in the sensors for a 2 year period in the Gulf of Mexico by looking at weekly composites. 

These results are being presented at the Ocean Optics Conference in Nov 02. 

We have implemented routine processing of MODIS-TERRA for selected ocean regions in the US (Gulf 

of Mexico, and Chesapeake Bay) and other areas. We have automated the procedure for transfer and 

processing of MODIS data from the “MODIS Direct Broadcasting” and the NOAA/NASA Project. We have 

obtained software and data from MODIS –AQUA but are not currently processing this data routinely.

We have added the capability to process MODIS AQUA data with the NRL Automated Processing 

System (APS), in addition to our capabilities for TERRA imagery. We have worked closely with folks at 

University of Miami, NOAA, and NASA/Goddard on this effort. We are implementing the new NIR 

algorithms we developed for SeaWiFS under SIMBIOS. The processing is now implemented and we process 

daily MODIS AQUA and TERRA imagery from several regions, including the Gulf of Mexico, Adriatic Sea, 

Persian Gulf, Arabian Sea, and New York Bight. 

We have automated transfer of MODIS data to our APS. Automated MODIS data streams are in place 

from both NASA/Goddard and NOAA, and we generally process scenes within about 6-8 hours of the satellite 

overpass. The processed imagery is archived at NRL/Stennis and is accessible via our web browser. 

We are working with NASA/Stennis to receive MODIS data over the Gulf of Mexico in real time. We

have successfully captured, transferred, and processed a test scene from their X-band receiving system. We

developed preliminary MODIS c660, absorption, scattering, and diver visibility products at 250m resolution 

for the Persian Gulf (in support of Operation Iraqi Freedom). Work is underway to improve the products and 

the atmospheric correction routines used for the 250m MODIS channels, including both Rayleigh and aerosol

corrections. 

Work is underway to implement new bio-optical optimization algorithms for MODIS into the NRL 

APS. We are in the process of validating MODIS TERRA-retrieved estimates of remote sensing reflectance 

(Rrs), through comparisons with our in situ shipboard measurements. After completing this effort, we will 

validate the derived optical properties (absorption, backscattering) for TERRA, then we will conduct 

similar comparisons for AQUA. We have implemented new SQL search engines on our web page to 

facilitate browsing for daily and composited MODIS scenes in our image archive. 

Other SeaWiFS activities 

• We have developed new methods to understand terrestrial flux from coastal rivers. By unravelling 

the ocean color signatures into the basic in water constituents; we developed a 2 year time series of 

164 
SIMBIOS Project Annual Report

SeaWIFS optical properties. We established the covariance of this 2 year satellite time series with

river discharge of the Mississippi river. 

 

• We developed new algorithms for extending the satellite near surface chlorophyll algorithms to 

depth. We assimilate the mixed layer depth and surface wind stress from NCOM with the latest 

satellite observations and define a vertical profile. This is being performed daily and has been

automated for the Gulf of Mexico. 

• We developed methods to limit cloud cover by using the spatially and temporally varying time series 

of satellite imagery. We developed methods of compositing more recent imagery into latest pixel 

composite. This method has created new products for optimizing ocean color products. We are using 

these methods for SeaWIFS and MODIS in the Gulf of Mexico. 

 

• We have used the data collected in SIMBIOS to validate the coastal algorithms. We have tested the 

algorithms used for bio-optical properties (absorption and scattering derived from SeaWIFS). We

have published this in Sea Technology. 

• We have organized a real time data base on our web server to provide real time access to ocean 

products. These include SeaWIFS and MODIS sensors. Real time access is available to test 

algorithm products and determine the sensitivity of the algorithms for SeaWIFS and MODIS. 

http://www7333.nrlssc.navy.mil

• We developed a relationship between absorption and salinity, to derive surface salinity maps from

coastal SeaWiFS imagery. 

• We developed a relationship between b(555) and total suspended solids (TSS), to derive surface 

TSS maps from SeaWiFS. 

• We linked physical processes (currents, tides, winds, wave resuspension) to optical distribution 

patterns in northern Gulf of Mexico. 

• We developed new algorithms to estimate particulate inorganic matter (PIM) and particulate 

organic matter (POM) from SeaWiFS imagery.

• We developed new SeaWiFS algorithms to estimate separate CDOM and detritus absorption 

coefficients, rather than a single combined term. This will facilitate development of a new optical 

water mass classification system. 

• We have improved coastal SeaWiFS optical algorithms through investigation of bb/b and F/Q 

parameters. 

• We have implemented a new Quasi- Analytical Algorithms (QAA) into the NRL APS processing of 

SeaWIFS and MODIS for coastal bio-optical algorithms. The QAA s a linearized version of the 

optimization algorithms which accounts for the majority of the optimization within the computation 

required. Products of chlorophyll, scattering and absorption from CDOM, phytoplankton and 

particles are currently being generated. They are available on the web. (Lee et al 2002). 

• We have developed automated methods for collection and processing in situ ocean optics data for 

processing of field data. These methods have been applied to all our advanced in situ

instrumentation. We have organized a calibration laboratory and track all our instrumentation. These 

include the ac9 and above water reflectance instruments and particle size instrument measurements. 

Additionally we have developed methods for partitioning particles into organic and inorganic 

fraction. These new methods are providing measurements for advanced algorithms. 

165 
SIMBIOS Project Annual Report

• Work is underway to implement new bio-optical optimization algorithms for SeaWiFS into the NRL 

APS. 

• We have developed new methods to understand terrestrial flux from coastal rivers, using new 

algorithms to estimate total suspended sediments (partitioned into organic and inorganic 

components). We calculated particle mass and areal plume extent, for the Mississippi River and 

Mobile Bay plumes. Results were presented at the Ocean Optics meeting in Santa Fe in November, 

2002, and at the Oceanography Society meeting in New Orleans in June, 2003. 

• We developed a new optical water mass classification system to trace water masses. The technique 

is based on the separation of the total absorption coefficient into components due to detritus, 

phytoplankton, and CDOM. It can be used to quantitatively characterize and trace coastal features 

over time. Results were presented at Ocean Optics and will be presented at the Optics of Natural 

Waters Conference in St. Petersburg, Russia, in September 2003. 

• Work is underway to implement algorithm changes into our APS, for consistency with changes made 

by NASA/Goddard during the 4th SeaWiFS reprocessing. Following the algorithm updates, we will 

validate the SeaWiFS Rrs and optical products through comparison with our shipboard in situ data. 

• We have implemented new SQL search engines on our web page to facilitate browsing for daily and

composited SeaWiFS scenes in our image data base. Work is underway to add the capability to 

search for level 1 imagery and in situ data from our web page, to facilitate validation and

reprocessing efforts (http://www7333.nrlssc.navy.mil). 

16.4 CONCLUSIONS 

Between the two groups, NOAA and NRL, we are developing an extensive data set for coastal and 

case 2 waters. We are in the process of merging the information for examination of remote sensing 

reflectance and chlorophyll. NRL has a data set from over 21 cruises. There are now 218 vertical profiles, 

263 field spectra, and 320 HPLCs. NOAA has approximately 100 same-day match-ups of remote sensing 

reflectance and chlorophyll from the southeast and Gulf of Mexico. We assembled a database of in situ

optical properties to evaluate SeaWIFS-derived properties collected from 1997 to present in a variety of 

coastal regions (Mississippi Bight, Mississippi River, West Florida Shelf, Loop Current, North Carolina, 

New Jersey). The data cover a broad range of absorption (0.4 – 15 m-1) and scattering (0.1 – 27 m-1) 

coefficients and remote sensing reflectance. The collected data set contributes to the SEABASS database, 

particular its Case 2 component. In addition to data collection, our groups produced the NIR-iterative 

technique used by the SeaWiFS project for the fourth reprocessing (repro4). 

 

REFERENCES 

Barnes, R.A., R.E. Eplee Jr., W.D. Robinson, G.M. Schmidt, F.S. Patt, S.W. Bailey, M. Wang, and C.R

McClain, 2000: The calibration of SeaWiFS on orbit, Earth Observing Systems V, William L. Barnes 

(eds.), Proceedings of SPIE Vol. 4135, 281-293. 

Carder, K.L., F.R. Chen, Z.P. Lee, S.K. Hawes, and D. Kamykowski, 1999: Semianlytical moderate

resolution imaging spectrometer algorithms for chlorophyll a and absorption with bio-optical domains 

based on nitrate-depletion temperatures, Journal of Geophysical Research, 104, 5403-5421. 

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. 

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. 

166 
SIMBIOS Project Annual Report

Gould, R.W. Jr., R.A. Arnone, and M. Sydor, 1998: Testing a new remote sensing algorithm to estimate 

absorption and scattering in CASE 2 waters, SPIE Ocean Optics XII, Hawaii, November, 1998. 

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, 195-206. 

Kahru, K., and G.B. Mitchell, 1999: Empirical chlorophyll algorithm and preliminary SeaWiFS validation 

for the California Current, International Journal of Remote Sensing, 20, 34323-3429. 

O'Reilly, J.E., S. Maritorena, G.B. 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, 24937-24953. 

Ransibrahmanakul, V., and R.P. Stumpf, 2001: Refining SeaWiFS Vicarious Calibration Using Spectra 

Slopes. American Geophysical Union, 2001 Fall Meeting, San Francisco, CA, December 10-14, 2001. 

Ransibrahmanakul, V., R.P. Stumpf, S. Ramachandran, H. Gu, R. Sinha, and K. Hughes: Evaluation of 

atmospheric correction and chlorophyll algorithms for processing SeaWiFS data. Seventh 

International Conference on Remote Sensing for Marine and Coastal Environments, Miami, FL. May

20-22, 2002. 

Ruddick, K., F. Ovidio, and M. Rijkeoboer, 2000: Atmospheric correction of SeaWiFS imagery for turbid

coastal and inland waters, Applied Optics, 39, 897-912. 

Siegel, D., M. Wang, S. Maritorena, and W. Robinson 2000: Atmospheric correction of satellite ocean 

color imagery: the black pixel assumption, Applied Optics, 39, 3582-3591. 

Sigel, S., 1956: Nonparametric statistics for the behavioral sciences. McGraw-Hill Book Company, New 

York. 

Stumpf, R.P., R.A. Arnone, R.W. Gould, P. Martinolich, V. Ransiabrahmanakul, P.A. Tester, R.G. 

Steward, A. Subramaniam, M.E. Culver, and J.R. Pennock, 2000: SeaWiFS ocean color data for US 

Southeast coastal waters. Sixth International Conference on Remote Sensing for Marine and Coastal

Environments. Charleston, SC. Veridian ERIM Intl. Ann Arbor, MI, USA, p. 25-27. 

Stumpf, R.P., R.A Arnone, R.W. Gould, Jr., P. Martinolich, V. Ransibrahmanakul, 2003: A PartiallyCoupled Ocean-Atmosphere Model for Retrieval of Water-Leaving Radiance from SeaWiFS in 

Coastal Waters: Chapter 9 In: Patt, F.S., and et al., 2003: Algorithm Updates for the Fourth SeaWiFS 

Data Reprocessing. NASA Tech. Memo. 2003-206892, Vol. 22, S.B. Hooker and E.R. Firestone, Eds., 

NASA Goddard Space Flight Center, Greenbelt, Maryland, 74 pp. 

Stumpf, R.P., M.E. Culver, P.A. Tester, M. Tomlinson, G.J. Kirkpatrick, B.A. Penderson, E. Truby, V. 

Ransibrahmanakul, M. Soracco. Monitoring Karenia brevis blooms in the Gulf of Mexico using 

satellite ocean color imagery and other data. Harmful Algae, 2, 147-160. 

This Research was Supported by the NASA 

Interagency Agreements # S-44796-X & S-44791-X 

Publications 

Arnone, R.A. and R.W. Gould, 2001: Mapping Coastal Processes with Optical Signatures, Backscatter, 12, 

17-24. 

167 
SIMBIOS Project Annual Report

Arnone, R.A, L. ZhongPing, P. Martinolich, and S.D. Ladner. Characterizing the optical properties of 

coastal waters by coupling 1 km and 250 m channels on MODIS Terra. Proceedings, Ocean Optics 

XVI, Santa Fe, New Mexico, 18-22 November, 2002. 

Bissett, W., H. Arango, R.A. Arnone, S. Glenn, C. Mobley, M.A. Moline, O.M. Scofield, R. Stewart, L. 

Sundman, and J. Wilkin. The Prediction Of Remote Sensing Reflectance At Leo-15. Proceedings, 

Ocean Optics XVI, Santa Fe, New Mexico, 18-22 November, 2002. 

Casey, B., R.A. Arnone, P.M. Martinolich, S.D. Ladner, M. Montes, D. Kohler, and W.P. Bissett.

Characterizing The Optical Properties Of Coastal Waters Using Fine And Course Resolution. 

Proceedings, Ocean Optics XVI, Santa Fe, New Mexico, 18-22 November, 2002. 

Davis, C. O., M. Moline, P. Bissett, R.A. Arnone, O. Schofield, B. Snyder, M. Montes, and B. Gao. Impact

Of Spatial Resolution On The Observation Of Coastal Environmental Features. Proceedings, Ocean 

Optics XVI, Santa Fe, New Mexico, 18-22 November, 2002. 

Gould, R.W., Jr., R.A. Arnone, and M. Sydor, 2001: Absorption, scattering, and particle size relationships 

in coastal waters: Testing a new reflectance algorithm, Journal of Coastal Research, 17, 328-341. 

Gould, R.W., Jr. and R.A. Arnone. 2003. Coastal Transport of Organic and Inorganic Matter. 2003 NRL 

Review. 

Gould, R.W., Jr. 2003. Optical water mass classification for coastal waters. NRL web page feature 

article, http://www.nrl.navy.mil/pressRelease.php?Y=2003&R=30-03r, and NRL Labstracts article, 

http://pipeline.nrl.navy.mil/publications/labstracts/issues/03-04-07.pdf.

Gould, R.W., Jr., and R.A. Arnone. 2002. Coastal optical properties estimated from airborne sensors. 

Remote Sensing of Environment, 79(1): 138-142. 

Gould, R.W., Jr., R.H. Stavn, M.S. Twardowski, and G.M. Lamela. Partitioning optical properties into

organic and inorganic components from ocean color imagery. Proceedings, Ocean Optics XVI, Santa 

Fe, New Mexico, 18-22 November, 2002. 

Gould, R.W., Jr. and R.A. Arnone. Optical Water Mass Classification for Ocean Color Imagery. 

Proceedings, Second International Conference, Current Problems in Optics Of Natural Waters, 8-12 

September, 2003, St. Petersburg, Russia. 

Gould, R.W., Jr., and R.A. Arnone. Temporal and spatial variability of satellite sea surface temperature 

and ocean color in the Japan/East Sea. International Journal of Remote Sensing, In Press. 

Haltrin, V.I., M.E. Lee, E.B. Shybanov, R.A. Arnone, A.D. Weidemann, and W.S. Pagau. Relationship 

between backscattering and beam scattering coefficients derived from new measurements of light

scattering phase functions. Proceedings, Ocean Optics XVI, Santa Fe, New Mexico, 18-22 November, 

2002. 

Haltrin, V.I., R.A. Arnone, and V.A. Urdenko. 2002. Effective Wavelength as a Universal Parameter of 

Hyperspectral Light Radiance Upwelling from the Sea,pp.133-142,in Ocean Optics: Remote Sensing 

and Underwater Imaging, Proceedings of SPIE,Vol.4488, Robert J. Frouin,and Gary D.Gilbert, 

Editors, Bellingham, WA, USA. 

Johnson, D.R., A.D. Weidemann, R.A. Arnone, and C.O. Davis. The Chesapeake Bay Outflow Plume and 

Coastal Upwelling Events Optical /Physical Properties. June 2001. Journal of Geophysical Research . 

168 
SIMBIOS Project Annual Report

Johnson, D., A. Weidemann, R.A. Arnone, W. Goode, R. W. Gould, Jr., and S. Ladner. Seasonal

variations in optical conditions associated with the Mobile Bay outflow plume. Proceedings, Ocean 

Optics XVI, Santa Fe, New Mexico, 18-22 November, 2002. 

Ladner, S., R.A. Arnone, R. Gould, A. Weidemann, V.I. Haltrin, Z. Lee, P. Martinolich, and T. Bergmann. 

Variability in the backscattering to scattering and F/Q ratios observed in natural waters. Proceedings, 

Ocean Optics XVI, Santa Fe, New Mexico, 18-22 November, 2002. 

Ladner, S., R.A. Arnone, R.W. Gould, Jr., and P.M. Martinolich. 2002. Evaluation of SeaWiFS optical

products in coastal regions. Sea Technology, 43(10): 29-35. 

Lee, Z., K.L. Carder, and R.A. Arnone. 2002. Deriving inherent optical properties from water color: A 

multiband quasi-analytical algorithm for optically deep waters. Applied Optics, Vol 41, No27, Sept

2002. pp 5755-5772. 

Martinolich, P.M., R.A. Arnone, and R.W. Gould. Coupling the MODIS and SeaWiFS optical Products. 

Proceedings, Ocean Optics XVI, Santa Fe, New Mexico, 18-22 November, 2002. 

Rochford, P. A., A. B. Kara, A.J. Walcraft, and R.A. Arnone. The Importance of Solar Subsurface 

Heating in Ocean General Circulation Models, Journal of Geophysical Research, Vol 106, No C12 

Dec 2001. pp 30923- 30938. 

Stavn, R.H., R.W. Gould, Jr., and G.M. Lamela. The biogeo-optical model: The database and testing. 

Proceedings, Ocean Optics XVI, Santa Fe, New Mexico, 18-22 November, 2002. 

Stumpf, R.P., M.E. Culver, P.A. Tester, M. Tomlinson, G.J. Kirkpatrick, B.A. Penderson, E. Truby, V. 

Ransibrahmanakul, M. Soracco, 2003. Monitoring Karenia brevis blooms in the Gulf of Mexico using 

satellite ocean color imagery and other data. Harmful Algae, 2: 147-160. 

Stumpf, R.P., R.A Arnone, R.W. Gould, Jr., P. Martinolich, V. Ransibrahmanakul. 2003. A PartiallyCoupled Ocean-Atmosphere Model for Retrieval of Water-Leaving Radiance from SeaWiFS in 

Coastal Waters: Chapter 9 In: Patt, F.S., and et al., 2003: Algorithm Updates for the Fourth SeaWiFS 

Data Reprocessing. NASA Tech. Memo. 2003--206892, Vol. 22, S.B. Hooker and E.R. Firestone, 

Eds., NASA Goddard Space Flight Center, Greenbelt, Maryland, 74 pp. 

Tester, P.A., S.M. Varam, M. Culver, D.L. Eslinger, R.P. Stumpf, R. Swift, J. Yungel, R.W. Litaker. 

Airrborne detection of ecosystem responses to an extreme event: phytoplankton displacement and 

abundance after hurricane induced flooding in the Pamlico-Albemarle Sound system, North Carolina, 

Estuaries, 26 (5), in press. 

Wynne, T.T., R.P. Stumpf, M.C. Tomlinson, V. Ransibrahmanakul, T.A. Villareal. 2003. A new method 

for detecting Karenia brevis blooms in the western Gulf of Mexico with satellite ocean color imagery. 

Proceedings of the 13th Biennial Coastal Zone Conference, Baltimore, MD, July 13-17, 2003; NOAA 

Coastal Services Center. NOAA/CSC/20322-CD. Charleston SC. 

Publications submitted

Ransibrahmanakul, V., and R.P. Stumpf. Absorbing aerosol correction for SeaWiFS. Submitted, Applied 

Optics. 

Wood, A.M., R.A. Arnone, R.W. Gould, W. Li, S. Pegau, and C. Trees. Optical Biogeography of 

Spectrally Distinct Forms of Picocyanobacteria in the Coastal Ocean. Abstract submitted to the 

International Photosynthetic Procaryote Meeting, August 2003. 

169 
SIMBIOS Project Annual Report

Presentations 

Arnone, R.A., R.W. Gould, Jr., C.O. Chan, and S.D. Ladner, 2000: Uncoupling CDOM, scattering and 

chlorophyll properties in coastal waters using SeaWiFS ocean color. Abstract Published to Oceans 

from Space 2000, Venice, Italy, 9-13 October, 2000. (Invited talk) 

Arnone, R.A., R.W. Gould, Jr., A.D. Weidemann, S.C. Gallegos, and V.I. Haltrin, 2000: Using SeaWiFS 

ocean color absorption, backscattering properties to discriminate coastal waters. Abstract published 

Ocean Optics XV, Monaco, 16-20 October, 2000. 

Arnone, R.A., R.W. Gould, Jr., S.D. Ladner, A. Weidemann, and T. Bowers. Seasonal Bio-Optical

Properties of the Coastal Gulf of Mexico and Influence of Riverine Inputs. ASLO Summer Meeting 

2002, Victoria, British Columbia, 10-14 June, 2002. 

Arnone, R.A., R.W. Gould, Jr., S.D. Ladner, B. Jones, P.J. Hogan, and G.A. Jacobs. Bio-Optical and 

Temperature Climatology of the Japan/East Sea. AGU/ASLO Ocean Sciences Meeting 2002, 

Honolulu, HI, 11-15 February, 2002. EOS Trans. AGU, 83(4), Ocean Sciences Meeting. Supplemental 

Abstract OS11C-41, 2002. 

Arnone, R.A., R.W. Gould, Jr., and S.D. Ladner. CDOM Variability in the Coastal Zone from SeaWiFS. 

AGU/ASLO Ocean Sciences Meeting 2002, Honolulu, HI, 11-15 February, 2002. EOS Trans. AGU, 

83(4), Ocean Sciences Meeting. Supplemental Abstract OS22J-01, 2002. 

Arnone, R.A., R.W. Gould, Jr., P.M. Martinolich, and S.D. Ladner. Improved algorithms for retrieving 

optical properties in coastal waters from ocean color sensors. Abstract submitted to the AGU Fall 

Meeting 2001, San Francisco, CA, 10-14 December, 2001. 

Arnone, R.A, Lee, ZhongPing, Martinolich, P. and Ladner, S.D “Characterizing the optical properties of 

coastal waters by coupling 1 km and 250 m channels on MODIS – Terra” Proceedings, Ocean Optics 

XVI, Santa Fe, New Mexico, 18-22 November, 2002. 

Arnone, R., R. Gould, Z. Lee, P. Martinolich, B. Casey, C. Hu, and A. Weidemann. Uncoupling the optical

signatures in coastal waters with ocean color sensors. Oceanography Society Annual Meeting, 4-6 

June, 2003, New Orleans, LA. 

Arnone, R.A., R.W. Gould, A. Weidemann, S.D. Ladner, and P. Martinolich. Satellite ocean color 

products for real-time navy applications. International Geoscience and Remote Sensing Symposium,

21-25 July, 2003, Toulouse, France. 

Bissett, W. Arango, H., Arnone, R.A. Glenn, S., Mobley, C., Moline, M.A. Scofield, O.M., Stewart, R.

Sundman, L., Wilkin, J. The Prediction Of Remote Sensing Reflectance At Leo-15, Proceedings, 

Ocean Optics XVI, Santa Fe, New Mexico, 18-22 November, 2002. 

Bowers, T., R.A. Arnone, S.D. Ladner, R.W. Gould, Jr., and D. Fox. Modeling the Vertical Bio-Optical

Properties Based on the Surface SeaWiFS Properties. Seventh International Conference on Remote 

Sensing for Marine and Coastal Environments, Miami, FL, 20-22 May, 2002. 

Casey, B., Arnone, R.A., Martinolich, P., Ladner, S.D., Montes. M., Kohler, D., Bissett, W.P., 

“Characterizing The Optical Properties Of Coastal Waters Using Fine And Coarse Resolution.” 

Proceedings, Ocean Optics XVI, Santa Fe, New Mexico,18-22 November, 2002. 

Culver, M.E., M.C. Tomlinson, T.H. Orsi, and R.P. Stumpf. Access to harmful algae data in the Gulf of

Mexico. 10th International Conference on Harmful Algae, St. Petersburg, FL. 21-25 October, 2002. 

170 
SIMBIOS Project Annual Report

Davis C, O , Moline, M. Bissett, P. Arnone, R.A. , Schofield, O., Snyder, B., Montes, M., Gao, Bo-Cai. 

Impact Of Spatial Resolution On The Observation Of Coastal Environmental Features, Proceedings, 

Ocean Optics XVI, Santa Fe, New Mexico, 18-22 November, 2002. 

Gould, R.W., Jr., R.A. Arnone, and C.O. Chan, 2000: Temporal and spatial variability of satellite sea 

surface temperature and ocean color. Oceans from Space 2000, Venice, Italy, 9-13 October, 2000. 

Gould, R.W. Jr., R.A. Arnone, C.M. Lee, and B.H. Jones, 2000: Characterizing surface and subsurface 

thermal and bio-optical fields in the Japan/East Sea during a spring bloom: Shipboard measurements

and satellite imagery. Proceedings, Ocean Optics XV, Monaco, 16-20 October, 2000. 

Gould, R.W., Jr., T.R. Keen, R.A. Arnone, R.H. Stavn, A.-R. Diercks, 2001: Bio-Optical variability in a 

barrier-island coastal environment: Coupling remote sensing imagery, shipboard measurements, and 

modeling results. Ocean Odyssey 2001 Meeting, Joint meeting of IAPSO and IABO, Mar del Plata, 

Argentina, 21-28 October, 2001. 

Gould, R.W., Jr., R.A. Arnone, S.D. Ladner, P.M. Martinolich, A.R. Diercks, and R.H. Stavn. Optical

Variability Linked to Physical Forcing in the Northern Gulf of Mexico using Satellite, Shipboard, and 

CODAR observations. AGU/ASLO Ocean Sciences Meeting 2002, Honolulu, HI, 11-15 February, 

2002. EOS Trans. AGU, 83(4), Ocean Sciences Meeting. Supplemental Abstract OS12J-06, 2002. 

Gould, R.W., Jr., R.H. Stavn, M.S. Twardowski, and G.M. Lamela. Partitioning optical properties into

organic and inorganic components from ocean color imagery. Proceedings, Ocean Optics XVI, Santa 

Fe, New Mexico, 18-22 November, 2002. 

Gould, R.W., Jr., R.A. Arnone, R. Smith, S. D. Ladner, and P. M. Martinolich. Coastal Transport of 

Organic and Inorganic Matter from Ocean Color Remote Sensing. Oceanography Society Annual 

Meeting, 4-6 June, 2003, New Orleans, LA. 

Greene, R.M., T.A. Villareal, R.P. Stumpf, K.A. Steidinger, J. Simons, J.R. Pennock, T. Orsi, C.A. 

Moncreiff, K. Hamilton, S.C. Gallegos, W.S. Fisher, and Q. Dortch: Harmful Algal BloomS 

Observing System (HABSOS) pilot project. 10th International Conference on Harmful Algae, St. 

Petersburg, FL. 21-25 October, 2002. 

Gould, R.W., Jr. and R.A. Arnone. Optical Water Mass Classification for Ocean Color Imagery. 

Proceedings, Second International Conference, Current Problems in Optics Of Natural Waters, 8-12 

September, 2003, St. Petersburg, Russia. 

Haltrin, V.I., W. McBride, and R.A. Arnone. A Spectral Approach to Calculate Spectral Reflection of Light

from a Wavy Water Surface, ONW 2001 International Conference, St. Petersburg, Russia, 25 Sept, 

2001. 

Haltrin, V.I., Lee., M.E., Shybanov, E.B., Arnone R.A. Weidemann A.D., and Pagau, W.S. Relationship 

between backscattering and beam scattering coefficients derived from new measurements of light

scattering phase Functions ” Proceedings, Ocean Optics XVI, Santa Fe, New Mexico,18-22 November, 

2002. 

Holderied, K., D. Pirhalla, V. Ransibrahmanakul, and R.P. Stumpf. 2003. Application of Improved 

SeaWiFS Chlorophyll Data in Estuarine and Coastal Waters. Presentation at the 2003 Estuarine 

Research Federation Conference, 15-18 Sep 2003, Seattle, Washington. 

Holt, A., R.P. Stumpf, M.C. Tomlinson, V. Ransibrahmanakul, V. Trainer, D. Woodruff, 2003. 

Applications of satellite ocean color imagery for detecting and monitoring harmful algal blooms in the 

Olympic Penisula region. Proceedings of the 13th Beennial Coastal Zone Conference, Baltimore, MD 

July 13-17, 2003. 

171 
SIMBIOS Project Annual Report

Hughes, D.C., R.J. Holyer, and R.A. Arnone. Removal of Bottom Reflectance Contribution to Remote

Sensing Reflectance in Coastal SeaWiFS Imagery AGU/ASLO Ocean Sciences Meeting 2002, 

Honolulu, HI, 11-15 February, 2002. EOS Trans. AGU, 83(4), Ocean Sciences Meeting Supplemental 

Abstract OS11C-41, 2002. 

Hughes, D.C., R.J. Holyer, and R.A. Arnone. Estimating Depth and Bottom Contributions Using 

Optimization and Neural Network Methods for Hyperspectral Imagery, Seventh International

Conference on Remote Sensing for Marine and Coastal Environments, Miami, FL, 20-22 May, 2002. 

Johnson, D., A. Weidemann, R. Arnone, W. Goode, R. W. Gould, Jr., and S. Ladner. Seasonal variations 

in optical conditions associated with the Mobile Bay outflow plume. Proceedings, Ocean Optics XVI, 

Santa Fe, New Mexico, 18-22 November, 2002. 

Jones, B.H., C.M. Lee, R.A. Arnone, R.W. Gould, and K. Brink. A Comparison of Bio-Optical

Characteristics of the Subpolar Front in the Japan/East Sea in Spring and Winter. AGU/ASLO Ocean 

Sciences Meeting 2002, Honolulu, HI, 11-15 February, 2002. EOS Trans. AGU, 83(4), Ocean 

Sciences Meeting Supplemental Abstract OS12O-04, 2002. 

Ladner, S.D., R.A. Arnone, R.W. Gould, Jr., and P.M. Martinolich. Evaluation of SeaWiFS bio-optical

products in coastal regions. Abstract submitted to the AGU Fall Meeting 2001, San Francisco, CA, 

10-14 December, 2001. 

Ladner, S., R. Arnone, R. Gould, A. Weidemann, V.I. Haltrin, Z. Lee, P. Martinolich, and T. Bergmann. 

Variability in the backscattering to scattering and F/Q ratios observed in natural waters. Proceedings, 

Ocean Optics XVI, Santa Fe, New Mexico, 18-22 November, 2002. 

Ladner, S., R.Gould, Jr., R. Arnone, A. Weidemann, P. Martinolich, and B. Casey. Coupling In Situ and 

Satellite Data to Validate Satellite Optical Properties. Oceanography Society Annual Meeting, 4-6 

June, 2003, New Orleans, LA. 

Lee, C.M., B.H. Jones, K.H. Brink, L.N. Thomas, R.A. Arnone, R.W. Gould, C. Dorman, and R.

Beardsley. Evidence of Wintertime Subduction at the Subpolar Front of the Japan/East Sea. 

AGU/ASLO Ocean Sciences Meeting 2002, Honolulu, HI, 11-15 February, 2002. EOS Trans. AGU, 

83(4), Ocean Sciences Meeting. Supplemental Abstract OS120-02, 2002. 

Ly, L.N., R.P. Stumpf, T.F. Gross, and F. Aikman III. Modeling of cross-shelf transport in support of 

monitoring and forecasting of harmful algal blooms along the West Florida coast, 10th International 

Conference on Harmful Algae. St. Petersburg, FL. October 21-25, 2002. 

Martinolich, P.M., R.A. Arnone, R.W. Gould, Jr., and S.D. Ladner. Coupling MODIS and SeaWiFS optical

products. Abstract submitted to the AGU Fall Meeting 2001, San Francisco, CA, 10-14 December, 

2001. 

Martinolich, P. Arnone, R.A. and Gould, R.W. “ Coupling the MODIS and SeaWIFS optical Products.” 

Proceedings, Ocean Optics XVI, Santa Fe, New Mexico, 18-22 November, 2002. 

Ransibrahmanakul, V., R.P. Stumpf, A. Robertson, K. Casey, and K. Buja, 2001: Temporal patterns in

chlorophyll and temperature around the coastal US, from September 1997 through 2001. 16th Biennial

Conference of the Estuarine Research Federation, St. Pete Beach, Fl, November 4-8, 2001. 

Ransibrahmanakul, V., R.P. Stumpf, S. Ramachandran, H. Gu, R. Sinha, and K. Hughes. Evaluation of 

atmospheric correction and chlorophyll algorithms for processing SeaWiFS data, Seventh International

Conference on Remote Sensing for Marine and Coastal Environments, Miami, FL. 20-22 May, 2002. 

172 
SIMBIOS Project Annual Report

Ransibrahmanakul, V., and R.P. Stumpf. Refining SeaWiFS Vicarious Calibration Using Spectra Slopes, 

American Geophysical Union, 2001 Fall Meeting. San Francisco, CA, 10-14 December, 2001. 

Ransibrahmanakul V., R.P. Stumpf, A. Robertson, and K.S. Casey. Temporal patterns in chlorophyll and 

temperature around the coastal US from September 1997 through 2001, Estuarine Research Federation, 

16th Biennial Conference. St. Pete Beach, FL. 4-8 November, 2001. 

Ransibrahmanakul, V., R. Stumpf, K. Holderied, P. Tester, and J.W. Budd. 2003. Retrieval of Chlorophyll

in U.S. Estuaries and Lakes. Presentation at the 2003 Estuarine Research Federation Conference, 15-

18 Sep 2003, Seattle, Washington. 

Sandidge, J., P.M. Martinolich, R.A. Arnone, S.D. Ladner, and R.W. Gould, Jr. Compositing Ocean Color 

Imagery, Seventh International Conference on Remote Sensing for Marine and Coastal Environments, 

Miami, FL, 20-22 May, 2002. 

Stavn, R.H., R.W. Gould, W.S. Pegau, G. Korotaev, and A.D. Weidemann. Biogeo-optics: Predicting the 

Optical Properties of Coastal New Jersey at the LEO-15 Site, AGU/ASLO Ocean Sciences Meeting 

2002, Honolulu, HI, 11-15 February, 2002. EOS Trans. AGU, 83(4), Ocean Sciences Meeting 

Supplemental Abstract OS31T-06, 2002. 

Stavn, R.H., R.W. Gould, Jr., and G.M. Lamela. The biogeo-optical model: The database and testing. 

Proceedings, Ocean Optics XVI, Santa Fe, New Mexico, 18-22 November, 2002. 

Stavn, R.H. and R.W. Gould, Jr. Biogeo_optics: Organic Matter Scattering in Case 2 Waters. 

Oceanography Society Annual Meeting, 4-6 June, 2003, New Orleans, LA. 

Stumpf, R.P. and M.E. Culver: Forecasting harmful algal blooms in the Gulf of Mexico, 2001: 16th

Biennial Conference of the Estuarine Research Federation, St. Pete Beach, Fl, November 4-8, 2001. 

Stumpf, R.P., M.E. Culver, E. Truby, and M. Soracco. A prototype system for monitoring and forecasting 

of harmful algal blooms in the Gulf of Mexico, 10th International Conference on Harmful Algae. St. 

Petersburg, FL. 21-25 October, 2002. 

Stumpf, R.P., and M.E. Culver. Forecasting harmful algal blooms in the Gulf of Mexico, 2001, 16th

Biennial Conference of the Estuarine Research Federation, St. Pete Beach, FL, 4-8 November, 2001. 

Stumpf, R.P., and M.E. Culver: Forecasting of Harmful Algal Blooms in the Gulf of Mexico. Seventh 

International Conference on Remote Sensing for Marine and Coastal Environments, Miami, FL. 20 -22 

May, 2002. 

Stumpf, R.P., J. Jurado, G. Hitchcock, P. Coble, J. Boyer, M. Neely, K. Steidinger, E. Truby, B. Roberts, C.

Hu, K. Carder, F. Muller-Karger, M.C. Tomlinson, B. Keller, and G. Feldman. Blackwater event in

Southwest Florida in winter 2002. Seventh International Conference on Remote Sensing for Marine 

and Coastal Environments, Miami, FL. 20-22 May, 2002. 

Stumpf, R.P., M.E. Culver, E. Truby, M. Soracco, M. Tomlinson, T.A. Villareal. Using satellite imagery to 

monitor harmful algal blooms in the Gulf of Mexico. Presentation at the 2003 Estuarine Research 

Federation Conference, 15-18 Sep 2003, Seattle, Washington. 

Tester, P.A., S. Varnam, R. Swift, R.P. Stumpf, D. Eslinger, M.E. Culver, M. Black. Hurricane Floyd: 

NOAA, NASA and new remote sensing technologies, 16th Biennial Conference of the Estuarine 

Research Federation, St. Pete Beach, FL, 4-8 November, 2001. 

173 
SIMBIOS Project Annual Report

Tester, P.A., R.P. Stumpf, V. Ransibrahmanakul, R.N. Swift, D. Eslinger, and M.E. Culver. Cumulative 

Effects of Three Hurricanes in One Season on Albemarle Sound and Pamlico Sound, North Carolina, 

American Society and Limnology and Oceanography, Victoria, BC, 10-14 June, 2002. 

Tester, P.A., R.W. Litaker. Airborne detection of ecosystem responses to an extreme event. Presentation at

the 2003 Estuarine Research Federation Conference, 15-18 Sep 2003, Seattle, Washington. 

Tomlinson, M.C., V. Ransibrahmanakul, R.P. Stumpf, E.W. Truby, G.J. Kirkpatrick, B.A. Pederson, G.A. 

Vargo and C.A. Heil. Evaluation of a method for detecting Karenia brevis blooms in the eastern Gulf 

of Mexico with satellite ocean color imagery, 10th International Conference on Harmful Algae. St. 

Petersburg, FL. 21-25 October, 2002. 

Tomlinson, M.C., V. Ransibrahmanakul, R.P. Stumpf, E. Truby, G.J. Kirkpatrick, B.A. Pederson, G.A. 

Vargo, and C.A. Heil. A retrospective evaluation of the use of SeaWiFS imagery to predict Karenia 

brevis blooms in the eastern Gulf of Mexico, Seventh International Conference on Remote Sensing for 

Marine and Coastal Environments, Miami, FL. 20-22 May, 2002. 

Tomlinson, M.C., T.T. Wynne, R.P. Stumpf, E.W. Truby, G.J. Kirkpatrick, B.A. Pederson, G.A. Vargo, 

T.A. Villareal. Remote sensing techniques for detecting harmful algal booms in coastal waters. 

Presentation at the 2003 Estuarine Research Federation Conference, 15-18 Sep 2003, Seattle, 

Washington. 

174 
SIMBIOS Project Annual Report

Chapter 17 

Varied Waters and Hazy Skies: Validation of ocean 

color satellite data products in under-sampled marine 

areas 

Ajit Subramaniam and R. Del Vecchio 

University of Maryland, College Park, Maryland 

Edward J. Carpenter 

San Francisco State University, California 

Douglas G. Capone 

University of Southern California, California 

17.1 INTRODUCTION 

It has been hypothesized that one of the limiting micronutrients for nitrogen fixation is Iron. It has also 

been hypothesized that aeolian dust is the primary source of Iron for near surface open ocean waters. As a 

part of two National Science Foundation funded studies – “Factors affecting, and impacts of, diazotrophic 

microorganisms in the western equatorial Atlantic” and “Oceanic N2 fixation and global climate” – we 

conducted 6 field surveys in the tropical and subtropical Atlantic and Pacific Oceans between 2001 and 

2003 (Table 17.1, Figures 17.1-17.6). The cruises were conducted during times of high and low aeolian 

dust activity in both ocean basins in order to study the impact of dust on nitrogen fixation. In addiction to 

dust, we also hypothesized that the Amazon River is a significant source of nutrients that stimulate nitrogen 

fixation, specifically Silicon and Iron that stimulate endosymbiotic diazotrophic cyanobacteria and cruises 

were also conducted to study the effects of the Amazon River on the phytoplankton species composition, 

the underwater light field and nutrient concentrations of the western Tropical Atlantic Ocean. 

Our SIMBIOS activity dovetailed on the NSF funded grants and the main objectives of our SIMBIOS 

funded project were to obtain field measurements of Aerosol Optical Thickness (AOT), normalized water 

leaving radiance (nLw), absorption due to dissolved (aCDOM), particulate (Ap), detrital (Ad) material, and 

phytoplankton pigments, under dusty skies, in the Amazon River plume, as well as in blooms of the marine 

diazotrophic cyanobacteria Trichodesmium. These measurements can then be used to validate satellite 

ocean color products such as those for chlorophyll concentration (chl a mg/m3), absorption due to 

chromophoric and detrital material (Acdm), reparameterize the algorithms for these products for specific 

locations such as the Amazon River plume, or to develop entirely new algorithms. In addition to the core 

SIMBIOS funded cruises, we also participated in six additional cruises in coastal waters where normalized 

water leaving radiance, aerosol optical thickness, absorption, and phytoplankton pigments were measured 

and data submitted to the SeaBASS archive. We also deployed a suite of optical instruments at a PIRATA 

mooring at 8N, 38W between April 2002 and July 2003 (Subramaniam 2002). These instruments are have 

just been recovered. After they have been recalibrated, the data will be analyzed to obtain a high temporal 

resolution time-series of normalized water leaving radiance at that site. This data will be compared to that 

derived from satellites as well as to study the potential enhancement of chlorophyll concentration at this 

site by Iron from aeolian dust.

175 
SIMBIOS Project Annual Report

17.2 RESULTS 

 

Several stations on the JAN01SJ cruise were sampled under dense dust storms (Figure 17.7). The 

AOT matchups showed reasonable agreement between satellite derived AOTs and the field measurements 

except at stations where the dust was so thick that there were no satellite retrievals (Figure 8). However, 

the SeaWiFS normalized water leaving radiance products did not compare well with in-situ measurements

and consequently the SeaWiFS chlorophyll product also performed very poorly. 

Figure 17.1: Cruise Track for JAN01SJ on monthly composite of SeaWiFS Chlorophyll map for Febuary 2001. 

Figure 17.2: Cruise Track for JUN01KN on monthly composite SeaWiFS map for August 2001. 

176 
SIMBIOS Project Annual Report

0.01 4.00 

Figure 17.3: Cruise Track for SEP02PAC on monthly composite SeaWiFS map for September 2002. 

Figure 17.4: Cruise Track for APR03ATL on monthly composite SeaWiFS map for April 2003. 

177 
SIMBIOS Project Annual Report

Figure 17.5: Cruise Track for JAN03ATL on monthly composite SeaWiFS map for January 2003. 

0.01 4.00

Figure 17.6: Cruise Track for JUL03PAC on monthly composite SeaWiFS map for August 2003 

We provided our field measurements of AOT and nLw to the University of Miami group to test their 

spectral matching algorithm. Our AOT dataset was also provided to Dr. Mahowald to test her dust

deposition model. We used the in-situ measurements of nLw to show that the OC4 algorithm appears to

work reasonably well in these waters in absence of the Amazon River plume (Figure 17.9). For the 

178 
SIMBIOS Project Annual Report

JUL01KN cruise, there were excellent match ups between nLw from satellites and the in-situ

measurements (Figure 17.10). Our initial analyses of field measurements of pigments and absorption

showed that the OC4 and the acd_gsm01 did not work in the Amazon River plume. However, as detailed 

below, we believe further work should allow us to develop specific algorithms for the Amazon River 

plume. Cloudiness, sun glint, orbital path, and tilt maneuvers did not allow for statistically meaningful 

comparison between satellite derived products and in-situ measurements for all our cruises. We are still 

analyzing the data from the last cruise that ended on the 23 of August 2003. 

Our field data on the underwater light field, the absorption properties, and the phytoplankton species 

composition show the extraordinary magnitude of the influence of the Amazon River on the marine 

ecosystem of the Western Tropical Atlantic Ocean. We found that the nitrate supplied by the Amazon 

River is taken up very quickly and the surface nitrate concentrations (in the plume) are below detection 

within a short distance from the mouth of the river. The phytoplankton population in this region is

composed mostly of coastal dinoflagellates and diatoms. After the nitrate is depleted, the phytoplankton 

population switches to one dominated by the diatom Hemiaulus with the endosymbiotic nitrogen fixing 

cyanobacteria Richelia. This organism appears to be dependent on the river plume for its Silicon

requirements and is found at greatest numbers at salinities between 26 PSS and 34 PSS. The nitrogen 

fixing cyanobacteria Trichodesmium spp. appears at greatest concentrations at salinities greater than 34

PSS when the Silicon is depleted. We explored the relationship between absorption due to CDOM and 

salinity because if a constant relationship between the two could be established, it would be possible to map 

the salinity in this region using satellite-derived estimates of aCDOM using algorithms such as acd_gsm01. 

Chromophoric dissolved organic matter (CDOM) is the optically active fraction of the dissolved 

organic matter and is one of the primary absorbing species in aquatic environments, thus plays an important

role determining the aquatic light field. CDOM absorption extends across the entire spectrum in a 

featureless exponential decay fashion. CDOM is also capable of emitting light as fluorescence upon 

excitation in the UV-visible spectral range. Thus, optical methods (absorption and fluorescence 

spectroscopy) have been widely used to study CDOM properties and distribution. Largest signal of CDOM

is always observed in coastal regions influenced by fresh water runoff that decreases towards offshore in a 

seasonal-dependent linear behavior. Recently, interest in CDOM has increased because of its potential 

interference with remote sensing detection of chlorophyll. Additionally, a relationship between CDOM and 

dissolved organic carbon (DOC) has been observed in coastal regions influenced by freshwater input, thus 

remote sensing of ocean color (i.e. CDOM) [and thus of organic carbon] has become a growing area of 

interest. 

The Amazon is the second longest river in the world after the Nile and it carries one sixth of the 

world's total river run-off, five times more than the second largest river. Once on the continental shelf, the 

Amazon water flows northward, first along the South American coast and then hundreds of kilometers into

the Open Ocean and profoundly influences the optical properties of the Western Tropical Atlantic Ocean. 

Nevertheless at the best of our knowledge a detailed study that describes the optical properties of the 

Amazon River plume has not yet being conducted. The limited data available at today (Green and Blough, 

1994) show water absorption coefficients inversely related to salinity and an extrapolated fresh water endmember (~ 4-6 m-1 at 355 nm) very similar to that of other large rivers and estuaries (see references in

Blough and Del Vecchio, 2002). 

Samples were collected using a CTD (conductivity-temperature-depth) rosette equipped with Niskin

bottles onboard of the R/V Seward Johnson in January-February 2001 and April-May 2003 and on the R/V 

Knorr in July-August 2001. For simplicity the cruises will be referred to as the winter, spring, and summer 

cruises respectively. Additional samples were collected with an acid cleaned bucket during the April-May

2003 cruise to increase the sample density near the river outflow. Salinity and temperature were obtained 

from the CTD system. All samples were immediately filtered through pre-combusted GF/F filters (0.7 μm 

pore-size) before further treatment. Samples for absorption were re-filtered through 0.2 μm pore-size nylon 

syringe filters to remove all particles and analyzed onboard. CDOM absorption spectra were acquired with 

a Shimadzu 2401-PC spectrophotometer employing a 10 cm optical cell. The instrument detection limit

(d.l.) was 0.002 OD. Milli-Q water was employed as the blank. Absorption spectra were recorded over the 

range 250-800 nm and the values at wavelengths 650-700 nm were averaged to determine the baseline. 

The spectral slope (S) was calculated by fitting the data to an exponential function over the range 300-700 

nm: S was accepted if the residuals of the fit fell within the instrument’s detection limit (0.046 m-1). 

179 
SIMBIOS Project Annual Report

During the winter cruise (February 2001), the Amazon River flow was low: surface waters in the 

region exhibited high salinity (> 34), low temperature (< 26 °C) and very low aCDOM(355) (close to our 

instrument detection limit, 0.046 m-1). The water column was vertically mixed with physical and optical

properties very similar all station and through the water column. During spring and summer, the river flow 

increased: surface waters closer to shore showed lower salinity (~24 in May 2003 

Figure 17.7: Dust storm blowing from north west Africa into the tropical Atlantic on 12 February 2001. 

Figure 17.8: Aerosol Optical Thickness - SeaWiFS vs Field Measurements

180 
SIMBIOS Project Annual Report

and ~ 28 in July 2001) and higher temperature (~28 °C in May 2003 and ~ 29 °C in July 2001) compared to

surface waters at offshore locales and to deeper waters. A 15-20-m-thick lens of fresh and warm water was 

observed during this period that moved offshore and remained constrained to the upper 20 meters where the 

pychnocline and the summer thermocline occurred. The water column became vertically stratified with 

inshore and offshore locales showing very different properties. Even the optical signal was very different

at inshore and offshore locales, with higher absorption values (and lower spectral slope, S) near the surface 

close-to-shore that decreased with depth and distance offshore. 

Early in the spring (April 2003), surface salinity was still high and aCDOM(355) was low in the region 

9N-52W to 11N-48W (stations 4-5-8-10), thus indicating that the river waters had not yet reached far 

offshore. Later on the same cruise, however, surface water with lower salinity and higher aCDOM(355) was 

observed closer to shore stretching northward along the continental shelf (7N-52W to 10N-57W, stations 

41 to 67). Surface waters with intermediate salinity and high aCDOM(355) was observed far offshore 

towards north-northwest (9N-54W to 13N-57W, stations 12 to 15). 

Early in the summer (July 2001), surface salinity was low and aCDOM(355) was high even at offshore 

locales (~12N-54W, station 27), thus suggesting that the plume now reached > 1000 kilometers offshore. 

Surface “filaments” of waters moving in different directions and giving rise to surface “blobs” were seen 

during the summer cruise in the satellite data (Figure 17.2). One filament was found to move northward 

along the south American coast while another filament retroflected and moved southeastward (stations 43to 

46 and 29 to 39). In addition, a North West blob (NW Blob) (~12.5N-57.5W, close to stations 13 and 22, 

for which no aCDOM(355) values are available) and a North East blob (NE Blob) (~12.5N-54W, close to

station 27) detached from the main river flow and moved northward. Each of these different water masses 

had different nutrient and optical properties and had differing phytoplankton populations. 

During both spring and summer, deeper waters (below the river plume, depth > 20 m) or surface 

waters outside the plume region showed high salinity and low aCDOM(355) typical of open ocean waters. 

During the winter, aCDOM(355) did not show any dependence on salinity (Fig. 17.11A): surface (yellow 

circles) and deep (grey circles) show similar and uncorrelated high salinity and low aCDOM(355) values. 

During the spring, aCDOM(355) for surface waters decreases linearly with salinity (Fig. 17.11B, green 

circles). However, slightly different dependences were observed earlier and later during the cruise [Fig. 

17.11B, compare bucket (green circles) to non-bucket (black-red circles) samples], probably associated 

with freshwater pulses of different intensities or to in situ production (see below). Most of the non-bucket

samples were taken just Northwest of the core of the river plume; some of these waters were characterized 

by intense bloom of Hemiaulus and by very high chlorophyll concentrations located near the surface (< 20 

m). The CDOM optical signal of these waters (Fig. 17.11B, black circles exhibited a shallower mixing line 

(Fig. 17.11B, black line) compared to the Amazon River mixing line (Fig. 17.11B, green line). Further, the 

CDOM absorption for some of these waters (Fig. 11B, stations labeled in blue) was similar in magnitude to

that of phytoplankton particulate material (not shown), unusual for surface oceanic waters. Some of these 

locales on the other hand, did not show high chlorophyll near the surface nor CDOM absorption 

comparable to phytoplankton particulate material despite departing from the Amazon River linear mixing 

line (Fig. 17.11B, red circles). Quite interestingly, during this cruise, some stations showed very different

surface properties (both physical and optical) when revisited over times while others showed very similar 

properties. Waters from below the plume (yellow circles) or from outside the plume (grey circles) show 

uncorrelated high salinity and low aCDOM values (Fig. 17.11B). 

During the summer, aCDOM(355) for surface waters decreases linearly with salinity (Fig. 17.11C, green 

circles). Surface waters from both filaments A and B follow the same linear dependence (Fig. 17.11C, 

green circles), thus suggesting simple dilution of the fresh water end-member into the salt water endmember (two end-member mixing model). Surface waters from the NE Blob on the other hand deviate 

from this aCDOM(355) to salinity linear dependence showing much lower aCDOM to salinity ratio (Fig. 

17.11C, blue circles). Samples collected close to the river mouth on August 6th 2001 (station 43) and 

further offshore on July 28th 2001 (station 29) (Fig. 17.11C) showed similar aCDOM(355) and salinity values;

this was also the case for station 44 relative to station 27 (Fig. 11C). Station 23 (Fig. 17.11C, black circles) 

was occupied for three days; a large diatom bloom (Hemiaulus) occurred close to the surface (~ 15 m)

causing a very high chlorophyll concentration of ~ 5-6 μg/L, much higher than the usual deep chlorophyll

maximum (~ 100 m) of ~ 1-2 μg/L. Samples from this station did not fall on the Amazon River mixing 

line that was observed for the spring cruise (black circles in Fig. 17.5B). Waters just below this bloom

(still within the fresh water plume) (~ 17 m) fell on the Amazon River mixing line (Fig. 17.11B, station

181 
SIMBIOS Project Annual Report

23:17m, green circle). Surface waters from stations 50 and 41 (Fig. 17.11C, orange circles) are 

characterized by a unique aCDOM/salinity ratio despite being located near the NE Blob and the filament B, 

respectively. Deeper water samples from below the plume (yellow circles) and samples from outside the 

plume (grey circles) were characterized by high salinity and low aCDOM values (Fig. 17.11C). We are 

continuing to analyze our data to better characterize and describe the relationship between aCDOM and 

salinity to better understand the nature of local sources and sinks of CDOM. 

Figure 17.9: Chlorophyll Concentration - Field measurements vs SeaWiFS and in-situ OC4 

Figure 17.10: Normalized Water Leaving Radiance - Field Measurements vs SeaWiFS 

182 
SIMBIOS Project Annual Report

Figure 17.11: Absorption of CDOM vs Salinity from field measurements

183 
SIMBIOS Project Annual Report

17.3 CONCLUSIONS 

 

Our analyzes show that great care need to be exercised while dealing with satellite ocean color data for 

vast areas of tropical Atlantic Ocean. Dust storms in late winter and early spring obscure the region 

preventing the retrieval of ocean color products or providing inaccurately high chlorophyll concentrations 

due to dust in the atmosphere. The Amazon River plume covers a large portion of this region between late

spring and early fall – for more than 6 months of the year – and the presence of absorption due to

chromophoric dissolved organic matter results in inaccurately high chlorophyll concentration retrievals in 

satellite imagery using standard global algorithms. Our analyzes show that while we still need further work

to understand the system, it may be possible to develop crude predictive capability for salinity using 

CDOM absorption algorithms tuned to the region. 

REFERENCES 

Blough, N.V. and Del Vecchio, R., 2002: Chromophoric Dissolved Organic Matter (CDOM) in the Coastal 

Environment. In: D. Hansell and C. Carlson (Editors), Biogeochemistry of marine dissolved organic 

matter. Academic Press, San Diego, pp. 509-546. 

Green, S.A. and Blough, N.V., 1994: Optical absorption and fluorescence properties of chromophoric

dissolved organic matter in natural waters. Limnol. Oceanogr. 39, 1903-1916. 

Subramaniam, A., E.J. Carpenter, and D.G. Capone, 2002: Varied Waters and Hazy Skies: Validation of 

ocean color satellite data products in under-sampled marine areas: II. In SIMBIOS Project 2002 

Annual Report. G.S. Fargion and C.R. McClain Eds. Pp 115-119. 

Table 17.1: Cruise Details

Cruise Name Dates Region Comments 

JAN01SJ 7 January – 22 February 2001 Tropical Atlantic Dust storms

JUN01KN 24 June – 18 August 2001 Tropical Atlantic Amazon River plume 

Ioffe 22 December 2001 – 9 January 2002 Drake Passage 

FEB02MB 26 – 28 February 2002 Massachusetts Bay Coastal waters 

APR02MB 4 – 6 April 2002 Massachusetts Bay Coastal waters 

APR02BR 27 March – 22 April 2002 Tropical Atlantic Oligotrophic waters 

JUL02MB 22 – 24 July 2002 Massachusetts Bay Coastal waters 

SEP02PAC 21 September – 16 October 2002 Subtropical Pacific Diazotrophic blooms 

OCT02MB 7 – 11 October 2002 Massachusetts Bay Coastal waters 

NOV02MB 18 – 19 November 2002 Massachusetts Bay Coastal waters 

JAN03ATL 4 – 18 January 2003 Tropical Atlantic Oligotrophic waters 

APR03ATL 18 April – 22 May 2003 Tropical Atlantic Amazon River plume 

JUN03MB 18 – 20 June 2003 Massachusetts Bay Coastal waters 

JUL03PAC 13 July – 22 August 2003 Subtropical Pacific Diazotrophic blooms 

184 
SIMBIOS Project Annual Report

Chapter 18 

HPLC Pigment Measurements For Algorithm 

Development and Validation in Support of the 

SIMBIOS Science Team 

Charles C. Trees and Jason R. Perl 

Center for Hydro-Optics and Remote Sensing, San Diego State University, California 

18.1 INTRODUCTION 

Remotely sensed ocean color is determined by the absorption and scattering properties of dissolved 

and suspended in-water constituents. For most oceanic and coastal areas phytoplankton, with their 

associated suite of pigments, dominate the optical signal viewed by satellite ocean color sensors. 

Phytoplankton use chlorophyll a as their major light harvesting pigment for carbon fixation, yet there are 

other accessory pigment compounds (chlorophylls b and c, carotenoids, and phycobiliproteins) that play a 

significant role in photosynthesis. These pigments have distinct spectral signatures, and therefore, changes 

in the pigment-algal assemblage result in spectral shifts in absorption and scattering. Accurate 

measurements of phytoplankton pigments are essential in understanding global carbon cycles in oceanic 

and coastal areas by minimizing satellite retrieval uncertainties when mapping these properties at regional 

to global scales. 

The focus of this 3-year program was to characterize phytoplankton pigments (excluding 

phycobiliproteins) in the water column during NASA’s Sensor Intercomparison and Merger for Biological 

and Interdisciplinary Oceanic Studies (SIMBIOS) program. This research was to provide high 

performance liquid chromatography (HPLC) pigment analysis on samples collected by the SIMBIOS 

Science Team for bio-optical algorithm validation and development. The effort provided the SIMBIOS 

program with an internally consistent pigment database of the highest quality to evaluate ocean color 

products. 

18.2 RESEARCH ACTIVITIES 

The HPLC method (Wright et al., 1991) that was used followed the protocols set in the Ocean Optics 

Protocols For Satellite Ocean Color Sensor Validation (Bidigare et al., 2002) and was designed to

incorporate a wide range of pigment concentrations from oceanic and coastal waters around the world. It 

used a reverse phase C18 column, with a tertiary solvent gradient. In addition, a temperature-controlled

autosampler insured continuous sample injection to maintain the quota of 4000 samples per year. System

calibration was routinely monitored and recorded each month to insure repeatability and consistency of 

data products. 

The chromatographic analyses did not separate monovinyl chlorophyll a from divinyl chlorophyll a, 

instead these pigments were quantified using dichromatic equations at 436 nm and 450 nm (Latasa et al., 

1996). Due to the large number of samples processed (4000 per year), the HPLC laboratory procedures 

were streamlined to accommodate the variety, and quantity of water samples collected by the SIMBIOS 

research community. By using larger HPLC solvent reservoirs, multiple sample tube racks, and running 

the system 24 hours a day, the laboratory could accommodate 500 samples a month, run continuously over 

a 3 to 4 week period. 

Extraction 

Pigment samples were extracted in 4.0 mL of 100% acetone, in which an internal pigment standard 

(canthaxanthin) was added to the acetone extract, prior to pipetting. This internal standard corrected for 

185 
SIMBIOS Project Annual Report

any extraction volume changes during sample processing. After 24 hours of extraction in a freezer 

(maintained at -20o

C), the samples were then sonicated for 10 seconds using an ultra-sonic microprobe tip

at a 60% duty cycle. The samples were then extracted for an additional 24 hours (stored in a freezer at -

20oC). Glass-fiber particulates, generated during sonication, were removed from the extract by centrifuging 

the samples at 5100 rpm, for 4 minutes, and subsequent filtration using 0.2 μm PTFE in-line filters. 

HPLC Analysis: Equipment, Solvents, and Methods 

Equipment: ThermoQuest HPLC system: Membrane Degasser, P4000 Quaternary pump; AS3000 

temperature controlled autosampler; UV6000 diode array detector (scanning from 190nm to 800nm at 1nm

resolution); FL3000 Fluroescence detector (Ex 404nm, Em 670nm) system controller with ChromQuest

(V3.0) software. 

Solvent Program: Solvent A: 80:20 methanol: 0.5M ammonium acetate (v/v); Solvent B: 90:10 acetonitrile: 

water (v/v); Solvent C: ethyl acetate. 

Gradient Program: [Linear, 1.0 mL min-1 flow rate] (Minutes, % SolvA, % SolvB, % SolvC) (0.0, 100, 0, 

0); (2.0, 0, 100, 0); (2.6, 0, 90, 10); (13.6, 0, 65, 35); (20.0, 0, 31, 69); (22.0, 0, 100, 0); (25.0, 100, 0, 0). 

Pigments were separated on a reverse phase, Waters Spherisorb ODS-2 (5μ) C18 column (250mm 

length, 4.6mm ID), using a three solvent gradient system [Solvent A: 80:20 methanol: 0.5M ammonium

acetate (v/v); Solvent B: 90:10 Acetonitrile: water (v/v); Solvent C: ethyl acetate] at a flow rate of 1.0 mL

min-1. A ThermoQuest P4000 pump and AS3000 Autosampler (maintained at 4o

 C) were used for all 

samples processed. Each sample extract was mixed (605μL:195μL, extract: water), and 100μL of this mix

was injected onto the column. 

The separation of the various pigments required 25 minutes with the pigment peaks being detected by

an absorption detector; a ThermoQuest UV6000 scanning diode array detector (190 nm to 800 nm at 1 nm

resolution). Pigments were monitored at 436 nm and 450 nm, with peak retention time or 

spectrophotometric recognition used to assign peak identification. In addition, a ThermoQuest FL3000 

scanning fluorescence detector was used to detect the various chlorophyll degradation products, which 

occur at lower concentrations. Although the absorption peaks for monovinyl and divinyl chlorophyll a coelute, each compound absorbs differently at 436 nm and 450 nm and it was therefore possible to correct for 

the divinyl chlorophyll a contribution by monitoring changes in the ratio (450 nm:436 nm) as a function of 

changes in the divinyl percentage (Latasa et al., 1996). Accuracy for each pigment compound was based 

on availability of pigment standards and the selection of pigment specific extinction coefficients. 

The standard fluorometric method of Holm-Hansen et al. (1965) was used to calculate chlorophyll and 

phaeopigment concentrations on an aliquot (100μL) of the pigment extract. These concentrations were also 

corrected for extraction volume changes using the canthaxanthin internal standard. The Ocean Optics 

Protocols (Trees et al., 2002) for fluorometric chlorophyll a was followed. 

Calibration

Calibration standards were purchased from Sigma Chemical Company and from DHI, Institute for 

Water and Environment, Denmark. System calibrations were performed to determine individual standard 

response factors for each compound. Each pigment response factor was determined using a multipoint 

calibration. Pigment standard concentrations were provided by DHI, or verified spectrophotometrically

(for chlorophyll a and chlorophyll b) using published extinction coefficients. 

Validation 

Peak integration was performed using ChromQuest (V.3.0) software, and manually checked to insure 

proper integration of each peak. Retention time and spectral signatures of the standards were used to verify 

peak identification. Peak areas were quantified using software-aided integration. Response factors 

generated during the system calibration were used for final concentration calculations. 

186 
SIMBIOS Project Annual Report

18.3 RESEARCH RESULTS 

During this pigment analysis numerous pigment calibration standards were analyzed and response 

factors for each compound determined. A summary of the number of primary and secondary calibrations 

performed per year and the average response factors for those calibrations by compound are shown in

Tables 18.1 and 18.2, respectively. 

Table 18.1. Calibration summary during the SIMBIOS funded project. 

Calibrations Pigments 2001 2002 2003

Primary Chls a & b 6 11 7

Secondary Carotenoids 2 4 2

Table 18.2. Average response factors for the various pigment compounds analyzed. 

Wavelength Pigment n AVERAGE STD CV

PDA 436nm MVChl a 25 5.5469E-04 3.2238E-05 5.81%

PDA 450nm Chl b 23 4.2086E-04 1.8151E-05 4.31%

PDA 450nm Chl c3 4 1.5712E-04 4.6619E-06 2.97%

PDA 450nm Chl c2 4 1.3296E-04 7.8587E-06 5.91%

PDA 450nm Perid 7 3.1793E-04 7.7302E-06 2.43%

PDA 450nm But 8 2.4648E-04 1.1542E-05 4.68%

PDA 450nm Fuco 8 2.4654E-04 5.9950E-06 2.43%

PDA 450nm Hex 8 2.5317E-04 3.5938E-06 1.42%

PDA 450nm Pras 3 2.8431E-04 1.3954E-05 4.91%

PDA 450nm Viol 3 1.9097E-04 3.9281E-06 2.06%

PDA 450nm Diadin 7 1.5867E-04 3.1051E-06 1.96%

PDA 450nm Allo 4 1.6212E-04 7.9343E-06 4.89%

PDA 450nm Diato 6 1.5968E-04 1.5684E-06 0.98%

PDA 450nm Lut 3 1.6796E-04 2.0838E-06 1.24%

PDA 450nm Zea 7 1.7039E-04 5.6880E-06 3.34%

PDA 436nm DVChl a 3 4.0170E-04 3.6590E-05 9.11%

 During this period, 15 different PI’s/Projects submitted pigment samples for analysis. A list of number 

of samples analyzed by PI or Project is shown in Table 18.3. Included in these numbers is an estimate of 

the number of samples (~400) that will be submitted by three PI’s (D. Siegel, G. Mitchell and N. Nelson) in 

November 2003. 

Table 18.3. Number of samples by PI/Project received for pigment analysis at CHORS. 

Principle Investigator 2001 2002 2003 Total by PI 

Ajit Subramaniam 349 474 602 1425 

Bob Arnone 130 175 142 447 

Dave Siegel 285 806 506 1597 

Francisco Chavez 358 631 832 1821 

Greg Mitchell 363 818 973 2154 

Richard Stumpf 14 118 43 175 

Ru Morrison 44 200 0 244 

187 
SIMBIOS Project Annual Report

Yves Dandonneau 21 0 0 21 

Larry Harding 0 30 0 30 

Norm Nelson 0 12 0 12 

Karl Szekielda 0 30 36 66 

Glenn Cota 0 175 0 175 

Dariusz Stramski 0 0 131 131 

SeaHARRE-2 --- --- 53 53 

MOBY/MOCE 255 517 192 964 

TOTAL 1819 3986 3510 9315 

18.4 CONCLUSIONS

The HPLC and fluorometric pigment analyses listed above have been processed and sent to the various 

PI’s for validation and verification. In addition a copy was also sent to the SIMBIOS Project at Goddard 

Space Flight Center and was added to NASA’s existing bio-optical database (SeaBASS). Plots of the 

SIMBIOS pigment data is shown in Figures 18.1 and 18.2, along with the entire pigment database acquired 

at CHORS over the past several decades. As can be seem the SIMBIOS data follows similar trends and in 

even some instances has reduced uncertainties. The range of pigment concentrations compared in these 

figures (some phytoplankton culture experiments have been included) covers over 6 orders of magnitude 

(<0.001 to 1000 mg m-3). It seems that the log-linear relationship found by Trees et al. (2000) is quite

ubiquitous and robust, even when monovinyl and divinyl chlorophyll a concentrations were separately

quantified. 

Software and Equipment Problems 

Throughout the 3-year period there have been some software and hardware problems that limited or at

least delayed the processing of pigment samples for the various PI’s. Some of these problems by year are 

listed below: 

Year 2001 

No major software or equipment problems 

Year 2002 

Incompatibility between HPLC ChromQuest software and Windows NT. This required manually 

resetting the computer every 4 days to minimize system lockup and failures. FL3000 Fluorescence 

detector. Lamp failure resulting in fusing the lamp to the lamp socket. Shipped back to manufacturer for 

repair. UV6000 Diode Array detector. Degradation of the absorption signal caused by lamp decay, flow 

cell contamination and light path misalignment. 

188 
SIMBIOS Project Annual Report

Year 2003 

Pump and Autosampler were sent to manufacturer for repair. UV6000 Diode Array detector. Returned 

to manufacture for a lamp replacement and maintenance. Overall, lamp failure is high, due to the increased 

hours of usage. FL3000 Fluorescence detector. Lamp fused to the lamp socket again. Sent back to

manufacturer for repair. 

CHORS Pigment Data Base

y = 1.0819x0.9056

R2 = 0.9562

n = 20,297

0.0001

0.001

0.01

0.1

1

10

100

1000

0.0001 0.001 0.01 0.1 1 10 100 1000

HPLC Total Chlorophyll a (mg/m**3)

Figure 18.1: Comparison of HPLC measured total chlorophyll a (chlorophyllide a, allomer and epimer 

chlorophyll a and monovinyl and divinyl chlorophyll a) versus total HPLC measured accessory pigments. 

The plus symbols represent the 9,315 SIMBIOS processed pigment samples, where as the grey filled circles 

are a mixture of data from Trees et al. 2000, and additional pigment data from C. Trees, R. Bidigare, R. 

Goericke and R. Barlow. 

189 
SIMBIOS Project Annual Report

CHORS Pigment Data Base

y = 1.2028x0.9607

R2 = 0.959

n = 11,330

0.0001

0.001

0.01

0.1

1

10

100

1000

0.0001 0.001 0.01 0.1 1 10 100 1000

HPLC Total Chlorophyll a (mg/m**3)

Fig.ure 18.2: Comparison of HPLC measured total chlorophyll a (chlorophyllide a, allomer and epimer 

chlorophyll a and monovinyl and divinyl chlorophylls a) versus fluorometrically-measured chlorophyll.

The plus symbols represent the 9,015 SIMBIOS processed pigment samples, where as the grey filled circles 

are a mixture of data from C. Trees and R. Bidigare. 

Intercalibration and Correction Efforts 

Year 2001: Standard fluorometric method correction. This problem was caused by a CHORS analyst not

following NASA’s protocols (Trees et al., 2000). Problem was corrected by replacing the analyst and 

sending out corrected fluorometric data to the various PI’s. The SeaBASS database was also updated. 

Year 2002: Round Robin#1 with Dr. Larry Harding and Laurie Van Heukelem. (Horn Point Marine 

Laboratory). Chlorophyll c3 response factor was off by a factor of 10. This was determined by

comparing ratios of Chl c3: Chl a from previous acquired pigment data. It was then later confirmed by

DHI, Denmark, who had mislabeled the concentration of that pigment batch. Corrected values were 

sent to PI’s and updated in SeaBASS. Received a new digital Turner Designs Fluorometer from the 

SIMBIOS Project. Multiple calibrations were performed to document any differences between the 

new digital fluorometer and the older analog fluorometer. 

Year 2003: Round Robin#2 with Horn Point Marine Laboratory and Dr. Norm Nelson (UCSB). 

SeaHARRE-2 participation. Volume extract correction (5.2% overestimation). The NASA pigment 

190 
SIMBIOS Project Annual Report

protocols were found to have an error. An Addendum was drafted and sent to the SIMBIOS project. 

PI’s were notified of this error, and corrected values were sent to the, as well as the SIMBIOS Project. 

Adjusted the percentage of acetone of the extraction solution to improve peak shape and resolution for 

chlorophylls c2 & c3. The extraction solution was diluted from 100% to 95% acetone, based on 

experiments conducted at CHORS. Started to monitor samples at 664nm (in addition to 436nm and 

450nm). This wavelength provides for better peak resolution for chlorophylide a, pheophorbide a, and 

pheophytin a. This wavelength is also not sensitive to the other co-eluting carotenoid pigments, 

making integration of the peaks easier and more accurate. 

Problem with Sample Labeling and Ancillary Information 

The NASA protocols recommended the proper procedure for filtering and recording samples for HPLC 

submission. There were a variety of trends that developed over the contract period. Below are some of the 

major problems that were encountered. 

PI’s designated their own sample labeling scheme (complicated with multiple characters), even though 

we recommended using a CHORS number, with the PI’s initials, followed by a single number (e.g. 

CT0001; Charles Trees, sample# 0001). 

Sample ID numbers were written on the aluminum foil after the filter was placed in it. Many times, 

the pen poked through the foil, leaving an ink mark on the filter. (This ink co-elutes with the chlorophyll c 

peaks in the chromatograms). 

Some PI’s used 47mm diameter filters instead of the recommended 25mm. CHORS extraction

procedures assume that 25mm filters are used, and the method had to be modified to accommodate the 

larger filters. In addition, it was difficult to efficiently sonicate these large filters in the 4.0 mL of acetone 

extract. 

Filters were not folded correctly (in half with filtered side facing in as recommended). Some were 

folded exposing the filtered face. Others were crammed into cryo-vials, or folded multiple times (4 times). 

These filters had to be thawed slightly to extract, creating possible pigment degradation.

Filters contained large amounts of frozen water, which mean too much water was left on the filter when it 

was placed into LN2. This additional water affects peak shape, as well as contributes to the degradation of 

pigments. 

Filters did not contain enough pigment concentration to properly analyze on the HPLC system. The 

protocols recommended certain volumes to filter based on water column productivity (e.g. 4 liters for 

oligotrophic waters). 

Ancillary logs were either incomplete or contain too much information. Files had filter numbers that 

did not match those delivered to CHORS. In addition, there were duplicate filter numbers that were not

recorded by the PI’s. It was recommend that the filters be inventoried prior to shipping to CHORS. 

There were on a few occasions when dewars arrived thawed out. This was from defective a dewar or 

improper LN2 charging. Samples were shipped to CHORS without prior notice. 

REFERENCES 

Bidigare, R.R., L.Van Heukelem, and C.C. Trees, 2002: HPLC Phytoplankton Pigments: Sampling, 

Laboratory Methods, and Quality Assurance Procedures, Chapter 16 in: Mueller J.L. and G.S. Fargion 

(Eds.) Ocean Optics Protocols For Satellite Ocean Color Sensor Validation. NASA/TM-2002-

210004/Rev3-Vol2, NASA Goddard Space Flight Center, Greenbelt, MD. pp258-268. 

Holm-Hansen, O., C.J. Lorenzen, R.W. Holmes, and J.D.H. Strickland, 1965: Fluorometric determination 

of chlorophyll. J. du Cons. Intl. Pour l'Expl. de la Mer.,30, 3-15. 

Jeffrey, S.W., R.F.C. Mantoura, and S.W. Wright (eds.), 1997: Phytoplankton Pigments in Oceanography, 

Monographs on Oceanographic Methodology, UNESCO, 661 pp. 

Latasa, M., R. R. Bidigare, M. E. Ondrusek, and 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. 

191 
SIMBIOS Project Annual Report

Trees, C.C., R.R. Bidigare, D.M. Karl, L. Van Heukelm, and J. Dore, 2002: Fluorometric Chlorophyll a:

Sampling, Laboratory Methods, and Data Analysis Protocols, Chapter 17 in: Mueller J.L. and G.S. 

Fargion (Eds.) Ocean Optics Protocols For Satellite Ocean Color Sensor Validation. NASA/TM2002-210004/Rev3-Vol2, NASA Goddard Space Flight Center, Greenbelt, MD. pp269-283. 

192 
SIMBIOS Project Annual Report

Chapter 19 

Assessment, Validation, and Refinement of the 

Atmospheric Correction Algorithm for the Ocean Color

Sensors 

Menghua Wang

University of Maryland, Baltimore County, Baltimore, Maryland 

19.1 INTRODUCTION 

The primary focus of this proposed research is for the atmospheric correction algorithm evaluation 

and development and satellite sensor calibration and characterization. It is well known that the 

atmospheric correction, which removes more than 90% of sensor-measured signals contributed from

atmosphere in the visible, is the key procedure in the ocean color remote sensing (Gordon and Wang, 

1994). The accuracy and effectiveness of the atmospheric correction directly affect the remotely retrieved 

ocean bio-optical products. On the other hand, for ocean color remote sensing, in order to obtain the 

required accuracy in the derived water-leaving signals from satellite measurements, an on-orbit vicarious 

calibration of the whole system, i.e., sensor and algorithms, is necessary. In addition, it is important to

address issues of (i) cross-calibration of two or more sensors and (ii) in-orbit vicarious calibration of the 

sensor-atmosphere system. The goal of these researches is to develop methods for meaningful comparison 

and possible merging of data products from multiple ocean color missions. In the past year, much efforts 

have been on (a) understanding and correcting the artifacts appeared in the SeaWiFS-derived ocean and 

atmospheric produces; (b) developing an efficient method in generating the SeaWiFS aerosol lookup tables, 

(c) evaluating the effects of calibration error in the near-infrared (NIR) band to the atmospheric correction 

of the ocean color remote sensors, (d) comparing the aerosol correction algorithm using the singlescattering epsilon (the current SeaWiFS algorithm) vs. the multiple-scattering epsilon method, and (e) 

continuing on activities for the International Ocean-Color Coordinating Group (IOCCG) atmospheric 

correction working group. In this report, I will briefly present and discuss these and some other research 

activities. 

 19.2. RESEARCH ACTIVITIES AND RESULTS 

Earth Curvature Effects Measured by SeaWiFS 

This work has been described in an article by Wang (2003). It is a common fact that the Earth’s 

atmosphere is a spherical-shell atmosphere (SSA) rather than a plane-parallel atmosphere (PPA). Thus, the 

light scattered by the Earth’s atmosphere is governed by physics of the radiative transfer equation (RTE) 

with proper boundary conditions in the SSA system. In the satellite and aircraft remote sensing, however, 

the PPA model is usually assumed to compute the lookup tables and convert the sensor-measured signals to

the desired physical and optical quantities. The PPA assumption is a simple yet very good approximation 

for solar and sensor zenith angles < ~ 80°. Note that the solar and sensor zenith angles in here are defined 

as a measure at the local surface. In the PPA assumption, however, the solar zenith angles ≥ 90° is not

defined. When the solar zenith angles ≥ 90° in the PPA system, i.e., when the sun is below the horizon,

there will be no light scattered out to the top of the atmosphere (TOA) or at the bottom of the surface. We

would experience completely darkness in this situation. The ocean color satellite sensor Sea-viewing Wide 

Field-of-view Sensor (SeaWiFS) acquired imageries with the solar zenith angles ≥ 90° and provided good 

examples of the Earth’s curvature effects on the light scattering processing in the SSA system. 

193 
SIMBIOS Project Annual Report

On June 26, 2000, SeaWiFS has acquired imageries with significantly extended coverage, adding about

another 5 minutes to the normal 40 minutes recording duty cycle. This was a test for the SeaWiFS 

coverage capability and to find a proper additional coverage for the SeaWiFS routine operation. For the 

test purpose, the SeaWiFS coverage for that specific orbit was extended significantly. A specific SeaWiFS 

measurement line, in which the solar zenith angles varies from ~76° to ~94°, was extracted for study and 

compared with both the PPA and SSA model computations (Wang, 2003). These are briefly described in

here. 

To understand and interpolate the SeaWiFS measurements, I have carried out the various radiative 

transfer simulations for the SSA model using the backward Monte-Carlo method that was developed by

Ding and Gordon (Ding and Gordon, 1994). Figure 19.1 shows simulated results in comparing with the 

SeaWiFS measurements at wavelengths 443 and 670 nm for atmosphere composed of the molecules 

(Rayleigh scattering), molecules and aerosols, and molecules and water-clouds. In Fig. 1, the SSA 

simulations were carried out for the sensor located at the altitude of 705 km (SeaWiFS orbit) with the 

various SSA atmospheres. Detailed descriptions of the solar-sensor geometry in the SSA model can be 

found in Ding and Gordon (Ding and Gordon, 1994). For each Monte-Carlo simulation, ten million 

photons were used. 

Figs. 19.1(a) and 19.1(b) are comparison results for atmosphere composed of molecules (Rayleigh 

scattering) with various physical thickness of the atmospheric layer (from 10 km to 100 km). Results from

the plane-parallel atmosphere (indicated as “ppa”) are also provided for comparisons. Clearly, PPA model

makes significant errors (under-estimation) for the solar zenith angles > ~85°. Figs. 1(c) and 1(d) show 

comparison results for a two-layer SSA model with aerosols located at the bottom 2-km layer (mixed with

22% of molecules). The Shettle and Fenn (Shettle and Fenn, 1979) maritime aerosol model with relative 

humidity of 80% (M80) and aerosol optical thickness at 865 nm τ a (865) of 0.1 were used in simulations. 

Figs. 19.1(e) and 19.1(f) give comparison results for a two-layer SSA model with water-clouds located at

the bottom 2-km layer (mixed with 22% of molecules). These are simulations for the cloud optical

thickness τ c of 8 and an effective particle radius re of 8 μm with the various physical thicknesses of the 

SSA layer, e.g., symbol “20, 2 km” is results for a two-layer SSA atmosphere with total physical thickness 

of 20-km and 2-km layer at the bottom. Signals contributed from ocean waters are ignored in all these 

simulations. They are negligible in these cases. 

As shown in Fig. 19.2, the TOA radiances at the first part (solar zenith angles from ~76° to ~85°) can 

be best simulated with cloud optical thickness ~8, while the next part can be well approximated with the 

M80 model for aerosol optical thickness of 0.1. These are comparisons in qualitatively. It is interesting to

note that, however, for the very large solar zenith angles (> ~85°), the TOA radiance is very sensitive to the 

physical thickness of the atmospheric layer assumed in the simulation. Indeed, for the very large solar 

zenith angles, the TOA radiance is much more sensitive to the change of physical thickness of the layer 

than to the optical properties of the medium (e.g., compare results of Figs. 19.1(c) and 19.1(e)). This is

because, for the SSA model with the large solar zenith angles, photons that are exiting to the TOA through 

scattering process are limited by the solar-sensor geometry and physical thickness of the layer. The thicker 

the layer is, the larger the probability that photons can be scattered into the sensor viewing direction, 

thereby the larger the sensor-measured radiance. At the wavelength 443 nm, the effective thickness of the 

SSA layer is best approximated by ~40 km, while at the band 670 nm the SSA layer can be approximated 

by ~20 km. The Rayleigh optical thicknesses for the wavelength at 443 nm and 670 nm are ~0.234 and 

~0.045, respectively. This explains different effective physical thicknesses for the wavelengths 443 and 

670 nm at which photons can interact with molecules and particles in the radiative transfer process. Photon 

at the wavelength 443 nm “sees” thicker of the layer than photon at 670 nm because the Rayleigh optical 

thickness at 443 nm is significantly larger than that of 670 nm. For the PPA model, on the other hand, the 

physical thickness has no effects on the TOA radiance. 

194 
SIMBIOS Project Annual Report

Figure 19.1: The simulated results compared with the SeaWiFS measurements for the wavelengths of 443 

and 670 nm and for various cases: (a) & (b) the Rayleigh atmosphere, (c) & (d) the two-layer SSA with

aerosols located at the bottom 2-km layer (mixed with 22% of molecules), and (e) & (f) the two-layer SSA 

with water cloud located at the bottom 2-km layer (mixed with 22% of molecules). 

195 
SIMBIOS Project Annual Report

Figure 19.2: Errors in the derived water-leaving reflectance with atmospheric correction using the NIR

bands of (a) 765 and 865 nm and (b) 1000 and 1240 nm. 

196 
SIMBIOS Project Annual Report

Atmospheric Correction using the Longer NIR Bands

For the Case-2 waters, the ocean is usually not black at the near-infrared (NIR) wavelengths centered 

at 765 and 865 nm, which are used for the atmospheric correction for both SeaWiFS and MODIS to derive 

the ocean color products (Gordon and Wang, 1994). In these cases, the ocean contributions at the NIR are 

often mistakenly accounted as radiance scattering from atmosphere, thereby leading to over-correction of 

atmospheric radiance and underestimation of water-leaving radiance at the visible. Simulations have been 

carried out for the atmospheric correction using the longer NIR bands centered at 1000, 1240, and 1640 nm

for retrievals of the ocean color product in the coastal waters. At the longer NIR bands, ocean is black even 

for the Case-2 waters. Thus, the Case-2 bio-optical model that has strongly regional dependence is not

needed for the atmospheric correction. The atmospheric correction can be therefore operated routinely for 

the global coastal waters, and the water-leaving radiance spectrum from blue to the short NIR in the coastal 

regions can be derived. This scheme can also be applied to the cases of Case-1 waters with high

chlorophyll concentrations in which the ocean contributions at the short NIR bands (765 and 865 nm) are 

significant. The performance of the atmospheric correction using the longer NIR bands is compared with

that of the SeaWiFS and MODIS algorithm using the 765 and 865 nm bands. Figure 19.3 provides 

examples of comparison results with the longer NIR for the atmospheric correction. Fig. 19.3 (a) is results

of error in the derived water-leaving reflectance using the NIR bands of 765 and 865 nm, while Fig. 19.3 

(b) is results of atmospheric correction using the longer NIR bands of 1000 and 1240 nm. In both cases, 

the errors are all within ~0.001 for the derived water-leaving reflectance for wavelengths from UV to the 

blue. 

Figure 19.3: The Ångström exponents from the SeaWiFS 12 aerosol models compared with the in situ data

collected bye the NASA SIMBIOS project. 

197 
SIMBIOS Project Annual Report

Aerosol Model Validation

This is part of work reported in Knobelspiesse et al. (2003), and complete descriptions and results

analyses can be found in this paper. Here is brief description of the SeaWiFS aerosol models compared 

with the in situ measurements. 

One of the main purposes for the NASA SIMBIOS in situ aerosol data collection over oceans is to 

compare and validate aerosol products derived from the ocean color sensors (Wang et al., 2000), in

particular, to validate the aerosol models used for the data processing in deriving the ocean color products. 

The primary goals of the SeaWiFS mission are routine global ocean color measurements and ocean biooptical property data. In retrieving the ocean near-surface signals from sensor-measured radiances at the 

satellite, however, the atmospheric and surface effects must be removed. This is known as atmospheric 

correction (Gordon and Wang, 1994) which removes more than 90% of the sensor-observed radiance in the 

visible spectrum. The SeaWiFS atmospheric correction algorithm (Gordon and Wang, 1994) uses two 

near-infrared (NIR) bands to estimate the aerosol optical properties and extrapolate these into the visible 

spectrum where the ocean color products are derived. In this process, aerosol models are needed. 

The SeaWiFS 12 aerosol models are the Oceanic model with the relative humidity (RH) of 99% (O99), 

the Maritime model with RH of 50%, 70%, 90%, and 99% (M50, M70, M90, and M99), the Coastal model 

with RH of 50%, 70%, 90%, and 99% (C50, C70, C90, and C99), and the Tropospheric model with RH of 

50%, 90%, and 99% (T50, T90, and T99) (Wang, 2000). The Oceanic, Maritime, and Tropospheic aerosol 

models are from (Shettle and Fenn, 1979), while the Coastal model was introduced by (Gordon and Wang, 

1994). These aerosol models are all non- and weakly absorbing. Table 19.1 summarizes optical properties 

for these aerosol models. In Table 19.1, the Ångström exponent is the mean value for a given aerosol

model, while the single-scattering albedo is value at the wavelength 865 nm. The same atmospheric 

correction algorithm with similar aerosol model set is used for the retrieval of the ocean color products 

from the Moderate Resolution Imaging Spectroradiometer (MODIS) (Esaias et al., 1998; Salomonson et al., 

1989). 

The aerosol model is defined by its particle size distribution and refractive indices (real and imaginary

parts). The Ångström exponent is related to the particle size distribution. Low value of the Ångström

exponent indicates the large particle size, while high value of the Ångström exponent represents the small

particle size of aerosols. For a given aerosol particle size distribution (aerosol model), the Ångström

exponent is defined. Therefore, the Ångström exponent values obtained from the ground in situ

measurements can be used to indicate if an appropriate aerosol model set in terms of the particle size 

distribution is used for the SeaWiFS data processing. Note that, however, this only gives part of picture, 

i.e., the aerosol particle size distribution. To validate the aerosol model and give a complete picture, the 

refractive indices are also needed. The refractive indices (or aerosol single-scattering albedo) can be 

derived with the sky radiance measurements (Dubovik and King, 2000; Wang and Gordon, 1993). 

Figure 19.3 provides comparison results of the Ångström exponent from the SeaWiFS 12 aerosol

models with those of the in situ measurements. The Ångström exponent values for the SeaWiFS 12 aerosol

models are represented as vertical lines which are compared with the histograms from all in situ data 

collected by the NASA SIMBIOS project (Knobelspiesse et al., 2003). Clearly, the in situ Ångström

exponents obtained from maritime environment can be well represented with the SeaWiFS 12 aerosol 

models in the most of time. It appeared that, however, in the open ocean regions aerosol models with much 

larger particle sizes (very small Ångström exponent values) may still needed, e.g., aerosol models reported 

by (Porter and Clarke, 1997). On the other hand, in the coastal regions, aerosol models with low values of 

the single-scattering albedo (absorbing aerosols), e.g., dust aerosol models, are needed to handle these 

absorbing aerosol cases (Moulin et al., 2001). 

The IOCCG Atmospheric Correction Working Group Activity

I have been continuing on activities for the IOCCG atmospheric correction working group. The main 

objective of the working group is to quantify the performance of the various exiting atmospheric correction 

algorithms used for the various ocean color missions. Therefore, the derived ocean color products from

various ocean color missions can be meaningfully compared and possibly merged. 

Table 19.1. Characteristics of the 12 aerosol models for the SeaWiFS data processing. 

198 
SIMBIOS Project Annual Report

Aerosol Model Relative Humidity (%) Symbol Ångström

Exponent

Single Scattering 

Albedo (865 nm) 

Oceanic 99 O99 -0.086 1.0

Maritime 50, 70, 90, 99 M50-M99 0.091-0.502 0.981-0.999 

Coastal 50, 70, 90, 99 C50-C99 0.224-0.757 0.971-0.997 

Tropospheric 50, 90, 99 T50, T90, T99 1.185-1.519 0.930-0.987 

REFERENCES 

Ding, K., and H.R. Gordon, 1994: Atmospheric correction of ocean-color sensors: effects of the Earth's 

curvature, Appl. Opt., 33: 7096-7106. 

Dubovik, O., and M. King, 2000: A flexible inversion algorithm for retrieval of aerosol optical properties 

from Sun and sky radiance measurements, J. of Geophys. Res., 105: 20,673-20696. 

Esaias, W.E., M.R. Abbott, I. Barton, O.B. Brown, J.W. Campbell, K.L. Carder, D.K. Clark, R.L. Evans, 

F.E. Hodge, H.R. Gordon, W.P. Balch, R. Letelier, and P.J. Minnet, 1998: An overview of MODIS 

capabilities for ocean science observations, IEEE Trans. Geosci. Remote Sens., 36: 1250-1265. 

Gordon, H.R., and M. Wang, 1994:, Retrieval of water-leaving radiance and aerosol optical thickness over 

the oceans with SeaWiFS: A preliminary algorithm, Appl. Opt., 33: 443-452. 

Knobelspiesse, K.D., C. Pietras, G.S. Fargion, M. Wang, R. Frouin, M.A. Miller, A. Subramaniam, and 

W.M. Balch, 2003: Maritime aerosol optical properties measured by handheld sun photometers, 

Remote Sens. Environ. (Submitted). 

Moulin, C., H.R. Gordon, R.M. Chomko, V.F. Banzon, and R.H. Evans, 2001: Atmospheric correction of 

ocean color imagery through thick layers of Saharan dust, Geophys. Res. Letters, 28: 5-8. 

Porter, J.N., and A.D. Clarke, 1997: Aerosol size distribution models based on in situ measurements, J. of 

Geophys. Res., 102: 6035-6045. 

Salomonson, V.V., W.L. Barnes, P.W. Maymon, H.E. Montgomery, and H. Ostrow, 1989: MODIS:

advanced facility instrument for studies of the Earth as a system, IEEE Trans. Geosci. Remote Sensing, 

27: 145-152. 

Shettle, E.P., and R.W. Fenn, 1979: Models for the Aerosols of the Lower Atmosphere and the Effects of 

Humidity Variations on Their Optical Properties, AFGL-TR-79-0214, , U.S. Air Force Geophysics 

Laboratory, Hanscom Air Force Base, Mass. 

Wang, M., 2000: The SeaWiFS atmospheric correction algorithm updates, Vol. 9, NASA Tech. Memo. 

2000-206892, S.B. Hooker and E.R. Firestone, Eds., SeaWiFS Postlaunch Technical Report Series, 

NASA Goddard Space Flight Center, Greenbelt, Maryland. 

Wang, M., 2003: Light scattering from spherical-shell atmosphere: Earth curvature effects measured by

SeaWiFS, Eos, Transactions, American Geophysical Union (In press). 

Wang, M., S. Bailey, and C.R. McClain, 2000: SeaWiFS Provides Unique Global Aerosol Optical Property

Data, in Eos, Transactions, American Geophysical Union, pp. 197. 

199 
SIMBIOS Project Annual Report

Wang, M., and H.R. Gordon, 1993: Retrieval of the columnar aerosol phase function and single scattering 

albedo from sky radiance over the ocean: simulations, Appl. Opt., 32: 4598-4609. 

This Research was Supported by 

 the NASA Contract # 00203 

Publications 

Wang, M., 1999: Validation study of the SeaWiFS oxygen A-band absorption correction: Comparing the 

retrieved cloud optical thicknesses from SeaWiFS measurements, Appl. Opt., 38, 937-944. 

Wang, M., 1999: Atmospheric correction of ocean color sensors: Computing atmospheric diffuse 

transmittance, Appl. Opt. , 38, 451-455. 

Wang, M., 1999: A sensitivity study of SeaWiFS atmospheric correction algorithm: effects of spectral band 

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

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. 

Eplee, R. E., Jr., W. D. Robinson, S. W. Bailey, D. K. Clark, P. J. Werdell, M. Wang, R. A. Barnes, and

C.R. McClain, 2001: The calibration of SeaWiFS, Part 2: Vicarious techniques, Appl. Opt., 40, 6701-

6718. 

Wang, M. and S. W. Bailey, 2001: Correction of the sun glint contamination on the SeaWiFS ocean and 

atmosphere products, Appl. Opt., 40, 4790-4798. 

Wang, M., B. A. Franz, R. A. Barnes, and C. R. McClain, 2001: Effects of spectral bandpass on SeaWiFSretrieved near-surface optical properties of the ocean, Appl. Opt., 39, 343-348. 

Chou, M. D., P. K. Chan, and M. Wang, 2002: Aerosol radiative forcing derived from SeaWiFS-retrieved 

aerosol optical properties, J. Atmos. Sci., 59, 748-757. 

Gregg, W. W., M. E. Conkright, J. E. O'Reilly, F. S. Patt, M. Wang, J. Yoder, and N. Casey-McCabe, 

2002: NOAA-NASA Coastal Zone Color Scanner reanalysis effort, Appl. Opt., 41, 1615-1628. 

Wang, M. and H. R. Gordon, 2002: Calibration of ocean color scanners: How much error is acceptable in 

the near-infrared, Remote Sens. Environ., 82, 497-504. 

Wang, M., 2002: The Rayleigh lookup tables for the SeaWiFS data processing: Accounting for the effects 

of ocean surface roughness, Int. J. Remote Sens., 23, 2693-2702. 

Wang, M., 2003: An efficient method for multiple radiative transfer computations and the lookup table 

generation, J. Quant. Spectr. Rad. Trans., 78, 471-480. 

Wang, M., 2003: Correction of artifacts in the SeaWiFS atmospheric correction: Removing the 

discontinuity in the derived products, Remote Sens. Environ., 84, 603-611, 2003. 

Tanaka, T. and M. Wang, 2003: Solution of radiative transfer in anisotropic plane-parallel atmosphere, J.

Quant. Spectr. Rad. Trans. (In press). 

Wang, M., Extrapolation of the aerosol reflectance from the near-infrared to the visible: the singlescattering epsilon vs. multiple-scattering epsilon method, Int. J. Remote Sens. (In press). 

200 
SIMBIOS Project Annual Report

Wang, M., Light Scattering from Spherical-Shell Atmosphere: Earth curvature effects measured by

SeaWiFS, Eos, Transaction, American Geophysical Union, (In press). 

Other publications 

Wang, M., 1998: Applying the SeaWiFS atmospheric correction algorithm to MOS: Effects of spectral

band variations, Proc. the 4th Pacific Ocean Remote Sensing Conference, p88-91, Qingdao, China, 

July 28-31. 

Wang, M., 1998: A validation of the SeaWiFS O2 A-band absorption correction, Proc. the 4th Pacific 

Ocean Remote Sensing Conference, p32-36, Qingdao, China, July 28-31. 

Wang, M. and B. A. Franz, 1998: A vicarious intercalibration between MOS and SeaWiFS, Proc. 2nd 

International Workshop on MOS-IRS and Ocean Colour, p95-102, Berlin, Germany, June 10-12. 

Wang, M., S. W. Bailey, C. R. McClain, C. Pietras, and T. Riley, 1999: Remote sensing of the aerosol

optical thickness from SeaWiFS in comparison with the in situ measurements, Proc. Ocean color, land 

surface, radiation and clouds, aerosols: the contribution of new generation spaceborne sensors to global 

change studies, Méribel, France, WK1-O-08, p1-4, January 18-22. 

Wang, M., 2000: Effects of the sea surface wind speed on the SeaWiFS derived ocean color optical 

property data, Proc. the ONR's Ocean Optics XV [CD-ROM], Musée Océanographique, Monaco, 

October 16-20. 

Wang, M. and S. W. Bailey, 2000: Correction of the sun glint contamination, Proc. the 5th Pacific Ocean 

Remote Sensing Conference, p18-22, Goa, India, December 5-8. 

Liberti, G. L, F. D'Ortenzio, R. Santoleri, G. L. Volpe, M. Wang, C. R. McClain, and V. E. Cachorro 

Revilla, 2001: Validation of SeaWiFS aerosol products over the Mediterranean basin," Proc. 

EUMETSAT Meteorological Satellite Data Users' Conference, p18-22, Antaya, Turkey, October 1-5. 

Wang, M., 2002: Approaches of the aerosol correction algorithm: methods of the single-scattering vs. 

multiple-scattering epsilon," Proc. the ONR's Ocean Optics XVI [CD-ROM], Santa Fe, New Mexico, 

November 18-22. 

Wang, M., 2002: Example of aerosol model effects in SeaWiFS atmospheric correction," Proc. SPIE's 

Third International Asia-Pacific Symposium on Remote Sensing of the Atmosphere, Ocean, 

Environment, and Space, Vol. 4892, p87-94, Hangzhou, China, October 23-27. 

Presentations 

Chou, M. D., P. K. Chan, and M. Wang, 1999: Global aerosol radiative forcing derived from SeaWiFSinferred aerosol optical properties, The AGU Fall Meeting, San Francisco, California, December 13-

17. 

Wang, M. and S. W. Bailey, 2000: Correction of the sun glint contamination," The 5th Pacific Ocean 

Remote Sensing Conference, Goa, India, December 5-8. 

Wang, M., 2000: Effects of the sea surface wind speed on the SeaWiFS derived ocean color optical 

property data," The Ocean Optics XV, Musée Océanographique, Monaco, October 16-20. 

Wang, M., 2000: Effects of the vicarious calibration on the SeaWiFS derived aerosol optical properties, 

The AGU Spring Meeting, Washington, DC, May 30-June 3. 

201 
SIMBIOS Project Annual Report

202 

Wang, M., A. Isaacman, B. A. Franz, and C. R. McClain, 2001: A comparison study of the ocean color data 

derived from OCTS and POLDER, The AGU Fall Meeting, San Francisco, California, December 10-

14, 2001. 

Gales, J. M., B. A. Franz, and M. Wang, 2001: A three year intercomparison of oceanic optical properties 

from MOS and SeaWiFS, The AGU Fall Meeting, San Francisco, California, December 10-14. 

Wang, M., 2002: Examples of the aerosol model effects in the SeaWiFS atmospheric correction," SPIE's 

Third International Asia-Pacific Symposium on Remote Sensing of the Atmosphere, Ocean, 

Environment, and Space, Hangzhou, China, October 23-27. 

Wang, M., 2002: Approaches of the aerosol correction algorithm: Methods of the single-scattering vs. 

multiple-scattering epsilon," The Ocean Optics XVI, Santa Fe, New Mexico, November 18-22.