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Naval Research Laboratory Stennis Space Center Ocean Optics Section Code 7333

Naval Research Laboratory Stennis Space Center Ocean Optics Section Code 7333. HyCODE January 2003 Miami, FL. Richard W. Gould, Jr. Alan Weidemann Robert A. Arnone Vladimir Haltrin Don Johnson ZhongPing Lee Wesley Goode Nicole Herrin Sherwin D. Ladner Paul M. Martinolich

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Naval Research Laboratory Stennis Space Center Ocean Optics Section Code 7333

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  1. Naval Research Laboratory Stennis Space Center Ocean Optics Section Code 7333 HyCODE January 2003 Miami, FL Richard W. Gould, Jr. Alan Weidemann Robert A. Arnone Vladimir Haltrin Don Johnson ZhongPing Lee Wesley Goode Nicole Herrin Sherwin D. Ladner Paul M. Martinolich Brandon Casey Regina Smith

  2. Objectives Exploit hyperspectral remote sensing measurements to separate optical constituents and understand coastal optical dynamics • SeaWiFS/MODIS processing • Optical scales of variability • Particle size variability • Vertical structure from Rrs • PHILLS processing • Optical water mass classification • Organic/inorganic components • Mobile Bay outflow (time series) • time series • new IOP algorithms • variogram analyses • affect on VSF • relationships w/oceanography • link surface and subsurface • image mosaic (geolocation) • derive IOP’s • Trace plumes, features • temporal/spatial variability • contribution to scattering • 6 cruises

  3. Data Sets In Situ • LEO 2001 July, 32 Stations • LEO 2000 July, 67 Stations • NGOM, 08/02, 9 stations • NGOM, 05/02, 83 stations • NGOM, 03/02, 37 stations • NGOM, 12/01, 114 stations • NGOM, 09/01, 55 stations • NGOM, 05/01, 100 stations • WFS, 10/98, 60 stations Imagery • LEO and NYBight • Gulf of Mexico SeaWiFS: 11/97 - present MODIS: 01/01 - present

  4. Data Processing Status

  5. West Florida Shelf - 1998 N(h) 1 (h) =  [z(xi) - z(xi + h)]2 2N(h) i = 1 where z(x) is a regionalized variable and h is the separation vector (lag). C 0 27.0 26.5 -20 26.0 25.5 25.0 Depth -40 24.5 24.0 Temperature 23.5 -60 23.0 22.5 -80 22.0 0 20 40 60 80 100 120 140 160 • Scales of Variability (variogram) A geostatistical technique to determine spatial correlation scales spatial, temporal • Surface/Subsurface Linkages Satellite and in situ

  6. LEO Mooring/Satellite Comparison 3-month time series MODIS mooring Compare surface MODIS/SeaWiFS estimates with mooring measurements – Rrs, chlor, IOPs SeaWiFS

  7. Results of a quasi-analytical inversion algorithm [C] = 1.0 mg/m3 synthetic data non-Case I input QAA output retrieval retrieval try w/other data sets input input • The QAA works well for both coastal and oceanic waters • Compared inversion results with different numbers of spectral bands (15 is generally adequate)

  8. PHILLS/SeaWiFS Comparison -- LEO 2001, bb(555) calibrated, atmospheric and flat-field corrections, 10 m resolution, SeaWIFS bio-optical algorithm SeaWiFS resampled to PHILLS resolution PHILLS convolved to SeaWiFS bands, smoothed 31 July, 2001 • SeaWiFS/PHILLS r = 0.752 • High variability of bb in coastal waters • Significant optical variability within • 1.1 km pixel Apply new algorithms NIR, optimization, beam c/particle size retrieval

  9. TSS and Particle Size, LEO 2000 TSS 0(555) = 0.89 0(555) = 0.93 21s04 18s01 0(555) = 0.94 18s02 19s01 18s03 17s01 25ns025 22s04 24s04 25ns01 22s05 28s04 27s04 24ns1 17s02 red points = 1.69 (highest tidal currents) green points = 1.50 (ebb tide or slack after low) blue points = 1.16 (flood tide or slack after high) • highest TSS: river, southern bay, bay mouth • lowest TSS: off-shore, northern bay smallest particles offshore, largest at bay mouth (resuspension by tidal currents) Red – highest TSS Green – mid Blue - lowest Relative Tidal Currents Tidal Effects – Little Egg Inlet

  10. Partitioning Optical Components aCDOM(412) ap(412) at-w(l) =f (Rrs(l)) aCDOM(412) = f (at-w(412)) aCDOM(l) = f (aCDOM(412)) adet(412) aj(412) ap(l) = at-w(l) - aCDOM(l) aj(l) =f (Rrs(l)) adet(l) = ap(l) - aj(l) estimate aCDOM(412) from at-w(412) estimate aCDOM(l) from aCDOM(412) aCDOM() = aCDOM(412) * 1.22189 e(-0.0167(-400))

  11. Water Mass Classification 20 May 2002 red pixels: relatively high detrital and CDOM absorption, lower phytoplankton absorption blue pixels: relatively high CDOM absorption, lower detrital and phytoplankton absorption green pixels: relatively high phytoplankton and CDOM absorption, lower detrital absorption 1 3 2 4 6 8 7 5 9 11 13 15 12 10 14 16 R: % adet(412) G: % aj(443) B: % aCDOM(412) Classify each pixel based on the percentages of adet(412), aCDOM(412), and aj(443) Location of classes 7, 8, and 9 on the image • Quantitative • Easily automated • Examine spatial/temporal • variability • Track water masses • Assess proportions of • optical constituents

  12. Partitioning Organic/Inorganic Components POM PIM bo(555) = (0.189 CHL0.751) * 0.97 * 660/555 (Loisel & Morel ,1998) Estimate POM from ap(443) and PIM from ap(412): POM = 4.09898 ap(443)0.56127 PIM = 8.33835 ap(412)0.89494 at higher particulate loads, POM levels off while PIM continues to increase – PIM will dominate in coastal areas May 20, 2002 Most scattering is due to inorganic particles 25-75% of TSS due to organic matter 5-25% of scattering due to organic matter

  13. Spectral Optical Scattering Cross Sections (m2/g), Suspended Mineral Matter, Mobile Bay, May 2002. b*pm(412) b*pm(440) b*pm(488) b*pm(510) 0.60±0.12 0.59±0.12 0.56±0.11 0.55±0.11 b*pm(532) b*pm(555) b*pm(650) b*pm(676) 0.53±0.10 0.52±0.10 0.38±0.12 0.44±0.08 b*pm(715) 0.42±0.08 THE BIOGEO-OPTICAL MODEL: THE DATABASE Robert H. Stavn Richard W. Gould, Jr. UNC, Greensboro NRL, Code 7333 Suspended mineral concentration and suspended organic concentration vs. bp(532), Mobile Bay, AL, May 2002 • Particulate mineral scattering cross sections -- the first determined directly from mineral mass concentration and the particle scattering coefficient; multiple regression of particulate scattering against the concentration of suspended mineral matter and suspended particulate organic matter. • The suspended mineral matter is the primary control of the volume scattering coefficient of coastal ocean waters. • Coastal ocean waters with significant concentrations of suspended mineral matter may affect prediction of the scattering coefficient of particulate organic matter from chlorophyll concentration. • Adequate optical models of coastal ocean waters and remote sensing algorithms require a geo-optical database for biogeo-optical modeling. Much more data are needed. ACKNOWLEDGMENTS RHS wishes to acknowledge the support of ONR Grant No. N00014-97-1-0812 and several previous ONR grants. RWG and RHS wish to acknowledge the support of the Naval Research Laboratory, Program Elements 61153N and 62435N.

  14. Using bb/b as a Surrogate to the VSF to Characterize Water Masses Note: Each point represents the average bb/b value for each wavelength (slope of the line from the plot in section 1). Measured bb/b (532nm) ratio vs. Wavelength, for the two Gulf of Mexico Cruises. Measured bb/b (532nm) ratio vs. Wavelength, for the two New Jersey Cruises. • Two distinct water types for both Gomex and NJ regions. • bb/b ratios of the Northern Gulf of Mexico waters during 2002 (left) and the New Jersey 2000 (right) are similar to the (Petzold 1972) ratio. • bb/b ratio can vary widely from the (Petzold 1972) ratio. • Spectral shapes for both Gomex and NJ regions are nearly flat. • May be possible to use bb/b to characterize the VSF in different water types; agrees with the behavior seen in VSF measurements (Haltrin et al., OOXVI 2002).

  15. bb/b variability -- from VSF measurements Non-regional relationship between backscattering and scattering coefficients based on 874 data points. Variability may be related to differences in particle size and/or composition

  16. Upcoming Experiment April 2003 – Monterey Bay MODIS 250 m • Lower turbidity, biologically controlled • Leverage w/ongoing research in the bay (mooring, CODAR, biological, optical) • Multi-ship • PHILLS overflights • Modeling • Transects, 24-hour vertical time-series stations • Space available on ship ??

  17. Collaborative Efforts • SeaWiFS/MODIS/PHILLS -- optical property retrieval, variability (Bob, Curt, Bill S., Paul, …) • Scales of optical variability -- imagery and in situ (Rick, Bob, Curt, Mark, Oscar) • Optical water mass classification -- detrital, phytoplankton, CDOM (Rick, Bob, Oscar, …. ) • Mooring/satellite comparison -- Rrs, IOP’s (Grace, Tommy, Rick) • CDOM relationships -- salinity, river discharge (Rick, Bob, Grace, Tommy, Paula?) • Optical time series -- LEO (Grace, Tommy, Alan, Oscar, …); GOM (Don, Rick, Bob, Alan, Curt, …)

  18. Collaborative Projects • VSF parameterization -- impact on Rrs, inversion from Rrs (Alan, Vlad, Ping, Bob, Scott, Emmanuel, …) • Organic/Inorganic partitioning --variability, optical cross-sections, impact on scattering (Rick, Bob S.,Gia) • Particle size -- affect on VSF, oceanographic variability (Rick, Vlad, Alan, Emmanuel, …) • Surface/Subsurface linkages -- imagery and in situ, WFS upwelling (Rick, Don, Paul, Bob W. …) • Assimilation of satellite bio-optics -- model development/validation (Bob, Paul, ….) • Bathymetry & bottom type algorithms -- apply optimization to LEO (Ping, Curt, ….)

  19. Publications (Articles/Proceedings, 2002-2003) articles/proceedings: 25 submitted/in preparation: 23 abstracts for upcoming meetings: 11

  20. Publications (Articles/Proceedings, 2002-2003)

  21. Publications (Articles/Proceedings, 2002-2003)

  22. Publications (Submitted/In Preparation, 2002-2003)

  23. Publications (Submitted/In Preparation, 2002-2003)

  24. Publications (Submitted/In Preparation, 2002-2003)

  25. Abstracts for Upcoming Meetings

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