Ocean Color, Remote Sensing, Ocean Color, Remote Sensing, and Oceanographic Education:and Oceanographic Education:

I. It’s Exciting!

II. Is it Too Good to be True?

James G. Acker

NASA Goddard Earth Sciences Data and Information Services Center (GES DISC)

Supporting Organizations and PeopleSupporting Organizations and People

Greg Leptoukh, Steve Kempler, GES DISC, Ocean Color Time-Series Project Co-I

Watson Gregg, Ocean Color Time-Series Project PI

Charles McClain, Gene Feldman, Wayne Esaias – Ocean Color Time-Series Project Co-Is (also responsible for CZCS, SeaWiFS, and MODIS)

GES DISC Staff (especially the Giovanni developers)

NASA project personnel

Part I: It’s Exciting!Part I: It’s Exciting!

• Satellite oceanography and remote sensing is cutting-edge, risk-taking, imagination-capturing science!

Satellite oceanography and remote sensing is highly visual

Blooms near Kamchatka

Hurricane Hurricane IsabelIsabel

HurricaneHurricaneFloyd Floyd sedimentssediments

Satellite sensors view where mortals cruise with caution

SeaWiFS monthly Level 3 data near South Georgia Island, January 1998

January 1998 cruise data January 1998 cruise data coveragecoverage

Satellites view unexpected phenomena in inaccessible places

MODIS-Aqua image of hydrogen sulfide eruption off the coast of Namibia, acquired June 3, 2005

Multiple data views illuminate a single phenomenon

Chlorophyll concentrationChlorophyll concentration

Sea Surface TemperatureSea Surface Temperature

Sea Surface HeightSea Surface Height

Researchers can perform mission-emulating data processing

ChlChlorophyll variability orophyll variability durduring the North Atlantic ing the North Atlantic blobloomom

AA sprispring bloom ng bloom in in thethe nnorthern orthern Red Red SeaSea

Smoke from pampas Smoke from pampas fires over the South fires over the South Atlantic Atlantic

SmokeSmoke

Giovanni (GES DISC Interactive Online Visualization and Analysis Infrastructure)

– the next step in oceanographic remote-sensing data visualization!

Northern Red Sea, August 1998, chl a

Northern RedSea, 1998

Latitude vs. time, chl a

Northern Red Sea, March 2000,chl a

NorthernRed Sea,2000

Latitude vs.time, chl a

Publication-quality analysis and Publication-quality analysis and graphics in minutes*graphics in minutes*

* To be discussed in Part II

Use of Giovanni in a study of the biological dynamics of the northern Red Sea supported a circulation model of this region which had virtually no other observational support

Making Giovanni even more powerfulMaking Giovanni even more powerfulClimatological anomaly analysis

Peru Current, 1997-1998 Winter: The classic El Niño effect

Effect of spring rains on the EastCoast (ref. Acker et al. 2005)

Exclusively for the workshop: anomaly analysis of the summer Orinico River plume

19981998 19991999 20002000 20012001 20022002

Intercomparison maps, Intercomparison maps, scatter plots, and time scatter plots, and time plots with multiple data plots with multiple data displaydisplay

Gulf of Mexico, January 2003:Gulf of Mexico, January 2003:SeaWiFS chlorophyll (color)SeaWiFS chlorophyll (color)MODIS-Aqua SST (contour)MODIS-Aqua SST (contour)

Box for plots at rightBox for plots at right

Time Plot, 2003, SST (green)Time Plot, 2003, SST (green)and chlorophyll (black)and chlorophyll (black)

Scatter plot, 2003, SST vs.Scatter plot, 2003, SST vs.LogLog1010 chlorophyll chlorophyll

Part II: Is It Too Good to be Part II: Is It Too Good to be True?True?• This is a halcyon era in oceanographic remote sensing, This is a halcyon era in oceanographic remote sensing,

particularly for ocean colorparticularly for ocean color

• Data is more widely available and simple to acquire (at Data is more widely available and simple to acquire (at least for some instruments)least for some instruments)

• Data tools are enabling data processing and analysis at Data tools are enabling data processing and analysis at all levels: beginner to advanced, student to professor all levels: beginner to advanced, student to professor

• The data is increasingly accurateThe data is increasingly accurate

• ““Acceptance” of remote-sensing data is increasingAcceptance” of remote-sensing data is increasing

• The data is being used in more complex ways; primary The data is being used in more complex ways; primary productivity estimation, physical-biological linkages, productivity estimation, physical-biological linkages, Hazardous Algal Bloom detection, suspended sedimentsHazardous Algal Bloom detection, suspended sediments

• For ocean color, moving from research to operationalFor ocean color, moving from research to operational

(following SST, SSH)(following SST, SSH)

Is this Era too good to be true?Is this Era too good to be true?

PLUS+PLUS+

• SeaWiFS Project/OBPG, CZCS heritage: decades of expertise SeaWiFS Project/OBPG, CZCS heritage: decades of expertise & dedication to data accuracy and validity& dedication to data accuracy and validity

• MODIS, MERIS: pushing the “state of the art” envelopeMODIS, MERIS: pushing the “state of the art” envelope

• Unique synergies of data producers (missions) with data Unique synergies of data producers (missions) with data archives (DAAC, NOAA/NESDIS, etc.), and data serversarchives (DAAC, NOAA/NESDIS, etc.), and data servers

• A large volume of free (no cost, no charge) dataA large volume of free (no cost, no charge) data

MINUS-MINUS-

• Funding threats (i.e., War of the Worlds)Funding threats (i.e., War of the Worlds)

• End of missions (no follow-ons to SeaWiFS or MODIS)End of missions (no follow-ons to SeaWiFS or MODIS)

• NPP/NPOESS VIIRS may have reduced capability and NPP/NPOESS VIIRS may have reduced capability and accuracy, and smaller programs dedicated to accuracy, and smaller programs dedicated to calibration/validationcalibration/validation

• Follow-ons to the Marine Optical Buoy (MOBY)?Follow-ons to the Marine Optical Buoy (MOBY)?

• New data might not be freeNew data might not be free

Is the data too good to be Is the data too good to be true?true?• It’s good; but not uniformly good (esp. ocean color in It’s good; but not uniformly good (esp. ocean color in

the coastal zone, under aerosols, mixed with the coastal zone, under aerosols, mixed with sediments or CDOM)sediments or CDOM)

• Improving remote-sensing data accuracy is hard; for Improving remote-sensing data accuracy is hard; for ocean color data, it’s REALLY hardocean color data, it’s REALLY hard

• How to handle missing data – the atmosphere is How to handle missing data – the atmosphere is always visible, but the ocean surface isn’t always visible, but the ocean surface isn’t

• Calibration/validation requires constant scrutiny, Calibration/validation requires constant scrutiny, and and as much sea-truth data as possibleas much sea-truth data as possible

• Data is just the first step; research requires Data is just the first step; research requires reference searching, error-checking, and expert reference searching, error-checking, and expert interpretation…interpretation…

… … because “analysis in minutes” increases the chance because “analysis in minutes” increases the chance of mis- and over-interpretation and spurious resultsof mis- and over-interpretation and spurious results

Teaching and research Teaching and research opportunitiesopportunities• Teach oceanography by interweaving concepts and Teach oceanography by interweaving concepts and

diagrams with actual data and observationsdiagrams with actual data and observations

• Teach oceanography by “doing”, i.e. use data tools Teach oceanography by “doing”, i.e. use data tools to create laboratory-type experiments (to create laboratory-type experiments (known known outcomeoutcome))

• Teach oceanography with guided research projects Teach oceanography with guided research projects (supplied topic, (supplied topic, unknown outcomeunknown outcome))

• Interact with data and data expertise (Interact with data and data expertise (LOCUSLOCUS))

• Become a Cal/Val site: accurate measurements of Become a Cal/Val site: accurate measurements of chlorophyll concentration coincident with satellite chlorophyll concentration coincident with satellite overflight are a validation point; more advanced overflight are a validation point; more advanced programs can do in-water and above-water optics programs can do in-water and above-water optics (both may be necessary to keep VIIRS honest) (both may be necessary to keep VIIRS honest)

The Laboratory for Ocean Color Users is the educational and outreach section of the Ocean Color Time-Series Project. LOCUS utilizes the expanding capabilities of Giovanni combined with SeaWiFS and MODIS-Aqua ocean color data, and the ocean color time-series data products when available. When fully developed, LOCUS will have the following components::

Tutorials (specific research topic demonstrations)

Giovanni Online User’s ManualEducational Modules (general concept coverage)

Concept-to-completion research project guidelinesCompleted research projects and publications User forum

and finally,ocean color remote-sensing imagery can be just plain beautiful