Ocean Color, Remote Sensing,
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 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!
• Satellite
oceanography and
remote sensing is
cutting-edge, risktaking, imaginationcapturing science!
Satellite oceanography and remote
sensing is highly visual Hurricane
Floyd
sediments
Hurricane Isabel
Blooms near Kamchatka
Satellite sensors view where
mortals cruise with caution
SeaWiFS monthly Level 3 data
near South Georgia Island,
January 1998
January 1998 cruise data
coverage
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 concentration
Sea Surface Temperature
Sea Surface Height
Researchers can perform missionemulating data processing
A spring bloom in
the northern Red
Sea
Smoke
Chlorophyll variability during
the North Atlantic bloom
Smoke from pampas
fires over the South
Atlantic
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
Red Sea,
March 2000,
chl a
Northern Red
Sea, 1998
Northern
Red Sea,
2000
Latitude vs.
time, chl a
Latitude vs.
time, chl a
Publication-quality analysis and
graphics in minutes*
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
* To be discussed
in Part II
Making Giovanni even more powerful
Climatological anomaly analysis
Peru Current, 1997-1998 Winter: The
classic El Niño effect
Effect of spring rains on the East
Coast (ref. Acker et al. 2005)
Exclusively for the workshop: anomaly analysis of the summer Orinico River plume
1998
1999
2000
2001
2002
Intercomparison maps,
scatter plots, and time
plots with multiple data
display
Time Plot, 2003, SST (green)
and chlorophyll (black)
Gulf of Mexico, January 2003:
SeaWiFS chlorophyll (color)
MODIS-Aqua SST (contour)
Box for plots at right
Scatter plot, 2003, SST vs.
Log10 chlorophyll
Part II: Is It Too Good to be True?
• This is a halcyon era in oceanographic remote sensing,
particularly for ocean color
• Data is more widely available and simple to acquire (at
least for some instruments)
• Data tools are enabling data processing and analysis at
all levels: beginner to advanced, student to professor
• The data is increasingly accurate
• “Acceptance” of remote-sensing data is increasing
• The data is being used in more complex ways; primary
productivity estimation, physical-biological linkages,
Hazardous Algal Bloom detection, suspended sediments
• For ocean color, moving from research to operational
(following SST, SSH)
Is this Era too good to be true?
PLUS+
• SeaWiFS Project/OBPG, CZCS heritage: decades of expertise &
dedication to data accuracy and validity
• MODIS, MERIS: pushing the “state of the art” envelope
• Unique synergies of data producers (missions) with data archives
(DAAC, NOAA/NESDIS, etc.), and data servers
• A large volume of free (no cost, no charge) data
MINUS• Funding threats (i.e., War of the Worlds)
• End of missions (no follow-ons to SeaWiFS or MODIS)
• NPP/NPOESS VIIRS may have reduced capability and accuracy, and
smaller programs dedicated to calibration/validation
• Follow-ons to the Marine Optical Buoy (MOBY)?
• New data might not be free
Is the data too good to be true?
• It’s good; but not uniformly good (esp. ocean color in
•
•
•
•
…
the coastal zone, under aerosols, mixed with sediments
or CDOM)
Improving remote-sensing data accuracy is hard; for
ocean color data, it’s REALLY hard
How to handle missing data – the atmosphere is always
visible, but the ocean surface isn’t
Calibration/validation requires constant scrutiny, and as
much sea-truth data as possible
Data is just the first step; research requires reference
searching, error-checking, and expert interpretation…
because “analysis in minutes” increases the chance of
mis- and over-interpretation and spurious results
Teaching and research opportunities
• Teach oceanography by interweaving concepts and
•
•
•
•
diagrams with actual data and observations
Teach oceanography by “doing”, i.e. use data tools to
create laboratory-type experiments (known outcome)
Teach oceanography with guided research projects
(supplied topic, unknown outcome)
Interact with data and data expertise (LOCUS)
Become a Cal/Val site: accurate measurements of
chlorophyll concentration coincident with satellite
overflight are a validation point; more advanced
programs can do in-water and above-water optics (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 Manual
Educational Modules (general concept coverage)
Concept-to-completion research project guidelines
Completed research projects and publications
User forum
and finally,
ocean color remote-sensing imagery can be just plain
beautiful