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Satellite Ocean Color Overview Dave Siegel – UC Santa Barbara With help from Chuck McClain, Mike Behrenfeld, Bryan Franz, Jim Yoder, David Antoine, Gene.

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Presentation on theme: "Satellite Ocean Color Overview Dave Siegel – UC Santa Barbara With help from Chuck McClain, Mike Behrenfeld, Bryan Franz, Jim Yoder, David Antoine, Gene."— Presentation transcript:

1 Satellite Ocean Color Overview Dave Siegel – UC Santa Barbara With help from Chuck McClain, Mike Behrenfeld, Bryan Franz, Jim Yoder, David Antoine, Gene Feldman, Claudia Mengalt, Bob Evans, Norm Nelson, Stéphane Maritorena & many more After ASLO 2011 Tutorial Talk

2 What is Satellite Ocean Color? The spectrum of the light reflected from the sea Water-leaving photons are backscattered & not absorbed (ocean optics & relationship to ecology) To see from space, we must account for the atmosphere (radiative transfer in the atmosphere) Ocean color signals are small (great measurements require great care…) ocean atmosphere

3 Bright Atmosphere – Dark Ocean TOA Radiance Water-Leaving Radiance Log(Radiance) Factor of >10 difference How do we correct for the atmosphere??

4 Atmospheric Correction Radiance budget for satellite radiance Measure L t ( ) Model L r ( ), L f ( ) & L g ( ) Unknowns are L w ( ), L a ( ) & L ar ( ) It is L w ( ) we need to know…

5 Atmospheric Correction Basics Goal: Subtract off the atmospheric path signals from the satellite measurement Model Rayleigh scattering, molecular absorption & interface reflectance terms Hard part is aerosol radiances –Use near-infrared bands to model aerosol radiances in the visible –Requires detailed models of aerosol optical properties that can be diagnosed from NIR

6 Ocean Color Sensor Requirements An ocean color sensor must… –Have necessary spectral resolution –Accurate (gains must be well known) –Stable (changes in gains must be known) –Well characterized (polarization, spectral, etc.) Devil is in the details…

7 Products SWIR NIR Visible Ultraviolet 5 nm resolution (345 – 775 nm) 26 required “multispectral” bands 3 SWIR bands Absorbing aerosols Dissolved organics Phytoplankton pigments Functional groups Particle sizes Physiology Pigment fluorescence Coastal biology Atmospheric correction (clear ocean) Atmospheric Correction (coastal) & Aerosol/cloud properties SWIR Total pigment or Chlorophyll-a but major errors due to absorption by dissolved organics Atmospheric Correction/ MODIS chlorophyll fluorescence Atmospheric Correction (clear ocean) Atmospheric Correction (coastal)** NIR Visible Ultraviolet Products MODIS on Terra was launched in 2000, but does not yet provide science quality ocean data MODIS/Visible Infrared Imaging Radiometer Suite (VIIRS) SWIR bands are not optimized for oceans No Measurements CZCS ( ) SeaWiFS ( ) MODIS (2002- )* VIIRS * ** “Multispectral” Ocean Bands CZCS: 4 SeaWiFS: 8 MODIS: 9 VIIRS: 7 OES: “hyperspectral” bands + 3 SWIR Comparison of Spectral Coverage Chuck McClain (GSFC) ACE plan

8 SeaWiFS:

9 Satellite Sensor Gains Accuracy requirements mean that satellite gains need to be known to better than Accurate ground data are required End-to-end test -> vicarious calibration Changes in these gains must be monitored Lunar viewing or multiple on-board sources Other “issues” creep in (like changes in polarization sensitivity or spectral responses)

10 SeaWiFS Lunar Calibration Fred Patt, GSFC Used to monitor sensor gain changes over time Uncertainty about trend lines is ~0.1%

11 Marine Optical BouY Accurate source for vicarious calibration Used with models to set absolute gains Located off Hawaii and operational since 1996 Difficulty with glint & nadir looking satellites (MODIS) Other ground obs are used as well

12 Satellite Sensor Issues Accuracy requirements mean that satellite gains need to be known to better than Accurate ground data are required - MOBY+ End-to-end test -> vicarious calibration Changes in these gains must be monitored Lunar viewing or multiple on-board sources Other “issues” can creep in (like changes in polarization sensitivity or spectral responses) Reprocessing is key…

13 Bio-Optical Modeling Goal: Relate water-leaving radiance spectra to useful in-water properties Both empirical & semi-analytical approaches Need simultaneous measurements of water- leaving radiance & useful in-water properties

14 Global In Situ Data - SeaBASS All DataAOT ChlOptical Profiles

15 Bio-Optical Algorithm OC4v6 used for SeaWiFS Empirical Maximum Band Ratio of L wN ( )’s (443/555, 489/555 & 510/555) From GSFC reprocessing page following O’Reilly et al. [1998] JGR

16 Retrieves three relevant properties (CDM, BBP, Chl) Assumptions… – Relationship between L wN ( ) & properties is known – Component spectral shapes are constant - only their magnitudes vary – Water properties are known Results in a least squares problem for the 3 properties Model coefficients determined through a global optimization using field observations Maritorena et al. Applied Optics [2002] GSM Semi-Analytic Model

17 OC4v6 w/ SeaWiFS Global match-up data set of SeaWiFS & in situ Chl’s Regression & the fit slope are very good End-to-End Validation

18 Ocean Color Components Ocean color signals are small (great measurements require great care…) We must account for the atmosphere (radiative transfer in the atmosphere) Relate water-leaving radiance to bio-optical properties (ocean optics & relationship to ecology) Validate we can do this end-to-end through the entire system (this requires periodic reprocessing)

19 1.Use what we have to its fullest… – New algorithms and approaches with data in hand – Make VIIRS a success (this is a community responsibility) 2.Help in the planning for future missions – PACE/ACE, GeoCAPE, HyspIRI, … 3.Make satellite ocean color “carbon savvy” – Develop techniques for Net Community Production, C Export, N 2 Fixation, etc. Where do we go now…

20 Thank You for Your Attention!! Special Thanx to the NASA Goddard Ocean Biology Processing Group and the NRC Committee on Sustained Ocean Color Obs


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