PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux Comparison of ocean color atmospheric correction...

PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux

Comparison of ocean color atmospheric correction

approaches for operational remote sensing of turbid, coastal

watersJeremy Werdell

Bryan Franz

NASA Goddard Space Flight Center

13 Jun 2012


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



temporal & spatial variabilitysatellite sensor resolutionsatellite repeat frequencyvalidity 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 & CDOMcomplicates estimation of Rrs(NIR)

complicates BRDF (f/Q) correctionssaturation of observed radiances

anthropogenic emissions (NO2 absorption)

Chesapeake Bay Program

AERONET

COVE


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


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



the “black pixel” assumption (pre-2000):

a(NIR) = t(NIR) - g(NIR) - f(NIR) - r(NIR) - w(NIR)

t() = w() + g() + f() + r() + a()



0



the “black pixel” assumption (pre-2000):


calculate aerosol ratios, :

(748,869)

(,869)

t() = w() + g() + f() + r() + a()


a(869)

a(748)

a(869)

a()


≈

≈

0

(748,869)


no one uses the “black pixel assumption” anymore


what happens if we don’t account for Rrs(NIR) > 0?

use the “black pixel” assumption (e.g., SeaWiFS 1997-2000)


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. 2005MERIS 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

approaches to account for Rrs(NIR) > 0 sr-1 overlap


fixed aerosol & water contributions (ex: MUMM)

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


advantages:

accurate configuration leads to accurate aerosol & Rrs(NIR) retrievalsseveral configuration options: fixed values, climatologies, nearby pixelsmethod available for all past, present, & future ocean color satellites

disadvantages:

no configuration is valid at all times for all water massesrequires local knowledge of changing aerosol & water propertiesimplementation can be complicated for operational processing

advantages & disadvantages


use of NIR + SWIR bands

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


use of SWIR bands only

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



advantages:

“black pixel” assumption largely satisfied in SWIR region of spectrumstraightforward implementation for operational processing

disadvantages:

only available for instruments with SWIR bandsSWIR bands on MODIS have inadequate signal-to-noise (SNR) ratiosdifficult to vicariously calibrate the SWIR bands on MODISmust define conditions for switching from NIR to SWIR


bio-optical model to estimate Rrs(NIR)

estimate Rrs(NIR) using a bio-optical model

operational SeaWiFS & MODIS processing ~ 2000-present



advantages: method available for all past, present, & future ocean color missionsstraightforward implementation for operational processing

disadvantages:

bio-optical model not valid at all times for all water masses


summary of the three approaches

defaults as implemented in SeaDAS

jeremy

not sure we need this slide



(748,869)

(,869)

t() = w() + g() + f() + r() + a()

a(869)

a(748)

a(869)

a()


≈

≈

approaches to account for Rrs(NIR) > 0 sr-1 overlap

assign and/or Rrs(NIR)Hu et al. 2000Ruddick et al. 2000

model Rrs(NIR)Siegel et al. 2000Stumpf et al. 2003Lavendar et al. 2005Bailey et al. 2010Wang et al. 2012

SWIRWang et al. 2007

coupled ocean-atmChomko & Gordon 2001Stamnes et al. 2003Kuchinke et al. 2009


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




model a(670) = aw(670) + apg(670)

estimate bb(670) using Rrs(670), a(670), & G(670) [Morel et al. 2002]

€

Rrs (670) =G(670)bb (670)

a(670) + bb (670)




model a(670) = aw(670) + apg(670)


model h using Rrs(443) & Rrs(555) [Lee et al. 2002]

from Carder et al. 1999€

η=2.0 1−1.2 exp −0.9Rrs (443)Rrs (555)

⎛

⎝ ⎜

⎞

⎠ ⎟

⎡

⎣ ⎢

⎤

⎦ ⎥




model a(670) = aw(670) + apg(670)



estimate bb(765) using bb(670) & h

€

bb (765) = bbw (765) + bbp (670)670765

⎛ ⎝ ⎜

⎞ ⎠ ⎟η




model a(670) = aw(670) + apg(670)




reconstruct Rrs(765) using bb(765), aw(765), & G(765)

€

Rrs (765) =G(765)bb (765)

aw (765) + bb (765)

aw(765) = 2.85 m-1




model a(670) = aw(670) + apg(670)




reconstruct Rrs(765) using bb(765), aw(765), & G(765)

iterate until Rrs(765) changes by <2% (typically 3-4 iterations)



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; white Chl > 0.3 mg m-3



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



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

comparing approaches requires consistency


comparisons with MERIS CoastColour

SeaWiFSMODIS-AquaMERISin situ

Middle Bay2005-2007

Rrs(l) 412-670


comparisons with MERIS CoastColour

SeaWiFSMODIS-AquaMERISin situ

Middle Bay2005-2007

derived productsChl, IOPs, Kd, TSM

Turbid Water Atmospheric Correction: rw(NIR) ≠ 0

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) estimate rw(NIR) from bb/(a+bb)

via Morel f/Q and retrieved Chla

guess rw(670) = 0

modelrw(NIR) = func rw(670)

Correctr'a(NIR) = ra(NIR) – t rw(NIR)

retrieve ri

w(670)

no

done

€

η =2.0 * 1. - 1.2 * e -0.9*R rs 443( ) R rs 555( )( )[ ]

€

a 670( ) = e ln Ca( )∗0.9389−3.7589( ) + aw 670( )

test|rw

i+1 (670) - ri(670)|

< 2%


SNR transect for MODIS-Aqua NIR & SWIR bands


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


MODIS-Aqua a(443)


distribution of the turbidity index using in NIR-SWIR


MODIS-Aqua vs. SeaWiFS

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

jeremy

not sure we need this slide


ocean color satellites view the top of the atmosphere

this signal includes contributions from:

Rayleigh (air molecules)

surface reflection

aerosols

water

model

model

to remove the aerosol signal, we make some assumptions about the “blackness” of the water signal in near-infrared (NIR) bands

0


PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux Comparison of ocean color atmospheric correction...

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