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Jeremy Werdell Bryan Franz NASA Goddard Space Flight Center 13 Jun 2012

Comparison of ocean color atmospheric correction approaches for operational remote sensing of turbid, coastal waters. Jeremy Werdell Bryan Franz NASA Goddard Space Flight Center 13 Jun 2012. outline. remote sensing of turbid, coastal waters is difficult

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Jeremy Werdell Bryan Franz NASA Goddard Space Flight Center 13 Jun 2012

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  1. Comparison of ocean color atmospheric correction approaches for operational remote sensing of turbid, coastal waters Jeremy Werdell Bryan Franz NASA Goddard Space Flight Center 13 Jun 2012 PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux

  2. outline remote sensing of turbid, coastal waters is difficult no one uses the “black pixel assumption” anymore most of the approaches to account for Rrs(NIR) > 0 sr-1 overlap a bio-optical model for Rrs(NIR) provides one viable approach comparing various approaches requires consistency PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux

  3. AERONET COVE remote sensing of turbid, coastal waters is difficult • temporal & spatial variability • satellite sensor resolution • satellite repeat frequency • validity of ancillary data (SST, wind) • resolution requirements & binning options • straylight contamination (adjacency effects) • non-maritime aerosols (dust, pollution) • region-specific models required? • absorbing aerosols • suspended sediments & CDOM • complicates estimation of Rrs(NIR) • complicates BRDF (f/Q) corrections • saturation of observed radiances • anthropogenic emissions (NO2 absorption) Chesapeake Bay Program PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux

  4. AERONET COVE remote sensing of turbid, coastal waters is difficult • temporal & spatial variability • satellite sensor resolution • satellite repeat frequency • validity of ancillary data (SST, wind) • resolution requirements & binning options • straylight contamination (adjacency effects) • non-maritime aerosols (dust, pollution) • region-specific models required? • absorbing aerosols • suspended sediments & CDOM • complicates estimation of Rrs(NIR) • complicates BRDF (f/Q) corrections • saturation of observed radiances • anthropogenic emissions (NO2 absorption) Chesapeake Bay Program PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux

  5. the experiment Chesapeake Bay provides our case study site run multiple long-term time-series of MODIS-Aqua Lower Chesapeake Bay, June 2002 - December 2008 processing configuration follows Reprocessing 2010 QC metrics: exclude cloudy days & high sensor zenith angles final analyses use ~ 13 days per month generate frequency distributions and monthly time-series use in situ measurements as reference consider potential for application in an operational environment PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux

  6. atmospheric correction & the “black pixel” assumption t() = w() + g() + f() + r() + a() TOA water glint foam air aerosols need a() to get w() and vice-versa PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux

  7. 0 atmospheric correction & the “black pixel” assumption t() = w() + g() + f() + r() + a() TOA water glint foam air aerosols need a() to get w() and vice-versa the “black pixel” assumption (pre-2000): a(NIR) = t(NIR) - g(NIR) - f(NIR) - r(NIR) - w(NIR) PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux

  8. 0 a(748) a(869) a() a(869) atmospheric correction & the “black pixel” assumption t() = w() + g() + f() + r() + a() TOA water glint foam air aerosols need a() to get w() and vice-versa the “black pixel” assumption (pre-2000): a(NIR) = t(NIR) - g(NIR) - f(NIR) - r(NIR) - w(NIR) calculate aerosol ratios,  : (748,869) (,869) ≈ ≈ (748,869) PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux

  9. no one uses the “black pixel assumption” anymore PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux

  10. no one uses the “black pixel assumption” anymore PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux

  11. what happens if we don’t account for Rrs(NIR) > 0? use the “black pixel” assumption (e.g., SeaWiFS 1997-2000) PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux

  12. approaches to account for Rrs(NIR) > 0 sr-1 overlap many approaches exist, here are a few examples: assign aerosols () and/or water contributions (Rrs(NIR)) e.g., Hu et al. 2000, Ruddick et al. 2000 use shortwave infrared bands e.g., Wang & Shi 2007 correct/model the non-negligible Rrs(NIR) Siegel et al. 2000 used in SeaWiFS Reprocessing 3 (2000) Stumpf et al. 2003 used in SeaWiFS Reprocessing 4 (2002) Lavender et al. 2005 MERIS Bailey et al. 2010 used in SeaWiFS Reprocessing 2010 Wang et al. 2012 GOCI use a coupled ocean-atmosphere optimization e.g., Chomko & Gordon 2001, Stamnes et al. 2003, Kuchinke et al. 2009 PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux

  13. fixed aerosol & water contributions (ex: MUMM) assign  & w(NIR) (via fixed values, a climatology, nearby pixels) PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux

  14. advantages & disadvantages advantages: accurate configuration leads to accurate aerosol & Rrs(NIR) retrievals several configuration options: fixed values, climatologies, nearby pixels method available for all past, present, & future ocean color satellites disadvantages: no configuration is valid at all times for all water masses requires local knowledge of changing aerosol & water properties implementation can be complicated for operational processing PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux

  15. use of NIR + SWIR bands use SWIR bands in “turbid” water, otherwise use NIR bands PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux

  16. use of SWIR bands only compare NIR & SWIR retrievals when considering only “turbid pixels” PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux

  17. advantages & disadvantages advantages: “black pixel” assumption largely satisfied in SWIR region of spectrum straightforward implementation for operational processing disadvantages: only available for instruments with SWIR bands SWIR bands on MODIS have inadequate signal-to-noise (SNR) ratios difficult to vicariously calibrate the SWIR bands on MODIS must define conditions for switching from NIR to SWIR PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux

  18. bio-optical model to estimate Rrs(NIR) estimate Rrs(NIR) using a bio-optical model operational SeaWiFS & MODIS processing ~ 2000-present PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux

  19. advantages & disadvantages advantages: method available for all past, present, & future ocean color missions straightforward implementation for operational processing disadvantages: bio-optical model not valid at all times for all water masses PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux

  20. summary of the three approaches defaults as implemented in SeaDAS PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux

  21. a(748) a(869) a() a(869) approaches to account for Rrs(NIR) > 0 sr-1 overlap t() = w() + g() + f() + r() + a() TOA water glint foam air aerosols coupled ocean-atm Chomko & Gordon 2001 Stamnes et al. 2003 Kuchinke et al. 2009 a(NIR) = t(NIR) - g(NIR) - f(NIR) - r(NIR) - w(NIR) (748,869) (,869) SWIR Wang et al. 2007 model Rrs(NIR) Siegel et al. 2000 Stumpf et al. 2003 Lavendar et al. 2005 Bailey et al. 2010 Wang et al. 2012 assign  and/or Rrs(NIR) Hu et al. 2000 Ruddick et al. 2000 ≈ ≈ PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux

  22. bio-optical model to estimate Rrs(NIR) initial Rrs(670) measured by satellite (using Rrs(765) = 0) PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux

  23. bio-optical model to estimate Rrs(NIR) initial Rrs(670) measured by satellite (using Rrs(765) = 0) model a(670) = aw(670) + apg(670) = 0.1 m-1 aw(670) = 0.44 m-1 PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux

  24. bio-optical model to estimate Rrs(NIR) initial Rrs(670) measured by satellite (using Rrs(765) = 0) model a(670) = aw(670) + apg(670) estimate bb(670) using Rrs(670), a(670), & G(670) [Morel et al. 2002] PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux

  25. bio-optical model to estimate Rrs(NIR) initial Rrs(670) measured by satellite (using Rrs(765) = 0) model a(670) = aw(670) + apg(670) estimate bb(670) using Rrs(670), a(670), & G(670) [Morel et al. 2002] model h using Rrs(443) & Rrs(555) [Lee et al. 2002] from Carder et al. 1999 PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux

  26. bio-optical model to estimate Rrs(NIR) initial Rrs(670) measured by satellite (using Rrs(765) = 0) model a(670) = aw(670) + apg(670) estimate bb(670) using Rrs(670), a(670), & G(670) [Morel et al. 2002] model h using Rrs(443) & Rrs(555) [Lee et al. 2002] estimate bb(765) using bb(670) & h PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux

  27. bio-optical model to estimate Rrs(NIR) initial Rrs(670) measured by satellite (using Rrs(765) = 0) model a(670) = aw(670) + apg(670) estimate bb(670) using Rrs(670), a(670), & G(670) [Morel et al. 2002] model h using Rrs(443) & Rrs(555) [Lee et al. 2002] estimate bb(765) using bb(670) & h reconstruct Rrs(765) using bb(765), aw(765), & G(765) aw(765) = 2.85 m-1 PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux

  28. bio-optical model to estimate Rrs(NIR) initial Rrs(670) measured by satellite (using Rrs(765) = 0) model a(670) = aw(670) + apg(670) estimate bb(670) using Rrs(670), a(670), & G(670) [Morel et al. 2002] model h using Rrs(443) & Rrs(555) [Lee et al. 2002] estimate bb(765) using bb(670) & h reconstruct Rrs(765) using bb(765), aw(765), & G(765) iterate until Rrs(765) changes by <2% (typically 3-4 iterations) PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux

  29. bio-optical model to estimate Rrs(NIR) black = land; grey = Chl < 0.3 mg m-3; white Chl > 0.3 mg m-3 not applied when Chl < 0.3 mg m-3 weighted application when 0.3 < Chl < 0.7 mg m-3 fully applied when Chl > 0.7 mg m-3 PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux

  30. bio-optical model to estimate Rrs(NIR) approaches used previously by the NASA OBPG: Bailey et al. 2010, Optics Express 18, 7521-7527 Stumpf et al. 2003, SeaWiFSPostlaunch Tech Memo Vol. 22, Chapter 9 Siegel et al. 2000, Applied Optics 39, 3582-3591 others PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux

  31. comparing approaches requires consistency comparison of approaches benefits from consolidation of software permits isolation of mechanisms & algorithms to evaluate limits interference by & biases of other factors (e.g., look up tables) for example Lavendar et al. 2005, Bailey et al. 2010, & Wang et al. 2012 all present bio-optical models for estimating Rrs(NIR) inclusion of all 3 into L2GEN permits isolated comparison of bio-optical model while controlling Rayleigh tables, aerosol tables, etc. uncertainties PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux

  32. comparisons with MERIS CoastColour SeaWiFS MODIS-Aqua MERIS in situ Middle Bay 2005-2007 Rrs(l) 412-670 PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux

  33. comparisons with MERIS CoastColour SeaWiFS MODIS-Aqua MERIS in situ Middle Bay 2005-2007 derived products Chl, IOPs, Kd, TSM PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux

  34. PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux

  35. guess rw(670) = 0 Turbid Water Atmospheric Correction: rw(NIR) ≠ 0 model rw(NIR) = funcrw(670) 1) convert rw(670) to bb/(a+bb) via Morel f/Q and retrieved Chla 2) estimate a(670) = aw(670) + apg(670) via NOMAD empirical relationship 3) estimate bb(NIR) = bb(670) (l/670)h via Lee 2010 4) assume a(NIR) = aw(NIR) 5) estimaterw(NIR) from bb/(a+bb) via Morel f/Q and retrieved Chla Correct r'a(NIR) = ra(NIR) – trw(NIR) retrieve riw(670) test |rwi+1(670) - ri(670)| < 2% no done

  36. SNR transect for MODIS-Aqua NIR & SWIR bands PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux

  37. Aqua Chl“match-ups” for NIR & SWIR processing PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux

  38. MODIS-Aqua a(443) PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux

  39. distribution of the turbidity index using in NIR-SWIR PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux

  40. MODIS-Aqua vs. SeaWiFS default processing ~ OC3 for MODIS-Aqua & OC4 for SeaWiFS PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux

  41. atmospheric correction & the “black pixel” assumption ocean color satellites view the top of the atmosphere this signal includes contributions from: Rayleigh (air molecules) surface reflection aerosols water model model 0 to remove the aerosol signal, we make some assumptions about the “blackness” of the water signal in near-infrared (NIR) bands PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux

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