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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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slide1

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

slide2

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

slide3

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

slide4

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

slide5

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

slide6

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

slide7

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

slide8

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

slide9

no one uses the “black pixel assumption” anymore

PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux

slide10

no one uses the “black pixel assumption” anymore

PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux

slide11

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

slide12

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

slide13

fixed aerosol & water contributions (ex: MUMM)

assign  & w(NIR) (via fixed values, a climatology, nearby pixels)

PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux

slide14

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

slide15

use of NIR + SWIR bands

use SWIR bands in “turbid” water, otherwise use NIR bands

PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux

slide16

use of SWIR bands only

compare NIR & SWIR retrievals when considering only “turbid pixels”

PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux

slide17

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

slide18

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

slide19

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

slide20

summary of the three approaches

defaults as implemented in SeaDAS

PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux

slide21

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

slide22

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

slide23

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

slide24

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

slide25

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

slide26

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

slide27

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

slide28

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

slide29

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

slide30

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

slide31

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

slide32

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

slide33

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

turbid water atmospheric correction r w nir 0

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

slide36

SNR transect for MODIS-Aqua NIR & SWIR bands

PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux

slide37

Aqua Chl“match-ups” for NIR & SWIR processing

PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux

slide38

MODIS-Aqua a(443)

PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux

slide39

distribution of the turbidity index using in NIR-SWIR

PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux

slide40

MODIS-Aqua vs. SeaWiFS

default processing ~ OC3 for MODIS-Aqua & OC4 for SeaWiFS

PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux

slide41

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