1. Ocean Color, Remote Sensing, and Oceanographic Education: I. I. It’s Exciting! II. II. Is it Too Good to be True? James G. Acker NASA Goddard Earth Sciences Data and Information Services Center (GES DISC)

2. 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

3. Part I: It’s Exciting! Satellite oceanography and remote sensing is cutting-edge, risk- taking, imagination- capturing science!

4. Satellite oceanography and remote sensing is highly visual Blooms near Kamchatka Hurricane Isabel HurricaneFloydsediments

5. Satellite sensors view where mortals cruise with caution SeaWiFS monthly Level 3 data near South Georgia Island, January 1998 January 1998 cruise data coverage

6. Satellites view unexpected phenomena in inaccessible places MODIS-Aqua image of hydrogen sulfide eruption off the coast of Namibia, acquired June 3, 2005

7. Multiple data views illuminate a single phenomenon Chlorophyll concentration Sea Surface Temperature Sea Surface Height

8. Researchers can perform mission- emulating data processing Chlorophyll variability during the North Atlantic bloom A spring bloom in the northern Red Sea Smoke from pampas fires over the South Atlantic Smoke

9. 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, 1998 Latitude vs. time, chl a Northern Red Sea, March 2000, chl a Northern Red Sea, 2000 Latitude vs. time, chl a

10. Publication-quality analysis and 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

11. 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 19981999200020012002

12. Intercomparison maps, scatter plots, and time plots with multiple data display Gulf of Mexico, January 2003: SeaWiFS chlorophyll (color) MODIS-Aqua SST (contour) Box for plots at right Time Plot, 2003, SST (green) and chlorophyll (black) Scatter plot, 2003, SST vs. Log 10 chlorophyll

13. Part II: Is It Too Good to be True? This is a halcyon era in oceanographic remote sensing, particularly for ocean color 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 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 Data tools are enabling data processing and analysis at all levels: beginner to advanced, student to professor The data is increasingly accurate The data is increasingly accurate “Acceptance” of remote-sensing data is increasing “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 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 For ocean color, moving from research to operational (following SST, SSH)

14. Is this Era too good to be true? PLUS+ SeaWiFS Project/OBPG, CZCS heritage: decades of expertise & dedication to data accuracy and validity SeaWiFS Project/OBPG, CZCS heritage: decades of expertise & dedication to data accuracy and validity MODIS, MERIS: pushing the “state of the art” envelope 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 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 A large volume of free (no cost, no charge) dataMINUS- 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 accuracy, and smaller programs dedicated to calibration/validation NPP/NPOESS VIIRS may have reduced capability and accuracy, and smaller programs dedicated to calibration/validation Follow-ons to the Marine Optical Buoy (MOBY)? Follow-ons to the Marine Optical Buoy (MOBY)? New data might not be free New data might not be free

15. 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) 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 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 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 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… 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

16. Teaching and research opportunities Teach oceanography by interweaving concepts and diagrams with actual data and observations 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 by “doing”, i.e. use data tools to create laboratory-type experiments (known outcome) Teach oceanography with guided research projects (supplied topic, unknown outcome) Teach oceanography with guided research projects (supplied topic, unknown outcome) Interact with data and data expertise (LOCUS) 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) 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)

17. 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

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