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PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux Comparison of ocean color atmospheric correction approaches for operational remote sensing of turbid, coastal.

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Presentation on theme: "PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux Comparison of ocean color atmospheric correction approaches for operational remote sensing of turbid, coastal."— Presentation transcript:

1 PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux 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

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 R rs (NIR) > 0 sr -1 overlap a bio-optical model for R rs (NIR) provides one viable approach comparing various approaches requires consistency PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux

3 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 R rs (NIR) complicates BRDF (f/Q) corrections saturation of observed radiances anthropogenic emissions (NO 2 absorption) Chesapeake Bay Program AERONET COVE

4 PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux 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 R rs (NIR) complicates BRDF (f/Q) corrections saturation of observed radiances anthropogenic emissions (NO 2 absorption) Chesapeake Bay Program AERONET COVE remote sensing of turbid, coastal waters is difficult

5 PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux 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

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

7 PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux 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)  t ( ) =  w ( ) +  g ( ) +  f ( ) +  r ( ) +  a ( ) atmospheric correction & the “black pixel” assumption TOA water glint foam air aerosols 0

8 PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux 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)  t ( ) =  w ( ) +  g ( ) +  f ( ) +  r ( ) +  a ( ) atmospheric correction & the “black pixel” assumption  a (869)  a (748)  a (869)  a ( ) TOA water glint foam air aerosols ≈ ≈ 0  (748,869)

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

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

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

12 PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux 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 R rs (NIR) Siegel et al. 2000used in SeaWiFS Reprocessing 3 (2000) Stumpf et al. 2003used in SeaWiFS Reprocessing 4 (2002) Lavender et al. 2005MERIS Bailey et al. 2010used in SeaWiFS Reprocessing 2010 Wang et al. 2012GOCI use a coupled ocean-atmosphere optimization e.g., Chomko & Gordon 2001, Stamnes et al. 2003, Kuchinke et al. 2009 approaches to account for R rs (NIR) > 0 sr -1 overlap

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

14 PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux advantages: accurate configuration leads to accurate aerosol & R rs (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 advantages & disadvantages

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

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

17 PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux 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

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

19 PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux 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

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

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

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

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

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

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

26 initial R rs (670) measured by satellite (using R rs (765) = 0) model a(670) = a w (670) + a pg (670) estimate b b (670) using R rs (670), a(670), & G(670) [Morel et al. 2002] model  using R rs (443) & R rs (555) [Lee et al. 2002] estimate b b (765) using b b (670) &  bio-optical model to estimate R rs (NIR) PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux

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

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

29 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 black = land; grey = Chl 0.3 mg m -3 bio-optical model to estimate R rs (NIR) PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux

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

31 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 R rs (NIR) inclusion of all 3 into L2GEN permits isolated comparison of bio-optical model while controlling Rayleigh tables, aerosol tables, etc. uncertainties comparing approaches requires consistency PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux

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

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

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

35 Turbid Water Atmospheric Correction:  w (NIR) ≠ 0 1) convert  w (670) to b b /(a+b b ) via Morel f/Q and retrieved Chl a 2) estimate a(670) = a w (670) + a pg (670) via NOMAD empirical relationship 3) estimate b b (NIR) = b b (670) ( /670) h via Lee 2010 4) assume a(NIR) = a w (NIR) 5) estimate  w (NIR) from b b /(a+b b ) via Morel f/Q and retrieved Chl a guess  w (670) = 0 guess  w (670) = 0 model  w (NIR) = func  w (670) model  w (NIR) = func  w (670) Correct  ' a (NIR) =  a (NIR) – t  w (NIR) Correct  ' a (NIR) =  a (NIR) – t  w (NIR) retrieve  i w (670) retrieve  i w (670) no done test |  w i+1 (670) -  i (670)| < 2%

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

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

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

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

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

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

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