Post on 17-Dec-2015
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
PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux
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
remote sensing of turbid, coastal waters is difficult
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
PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux
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
remote sensing of turbid, coastal waters is difficult
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
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
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
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)
PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux
no one uses the “black pixel assumption” anymore
PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux
no one uses the “black pixel assumption” anymore
PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux
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
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
PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux
fixed aerosol & water contributions (ex: MUMM)
assign & w(NIR) (via fixed values, a climatology, nearby pixels)
PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux
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
PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux
use of NIR + SWIR bands
use SWIR bands in “turbid” water, otherwise use NIR bands
PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux
use of SWIR bands only
compare NIR & SWIR retrievals when considering only “turbid pixels”
PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux
advantages & disadvantages
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
PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux
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
advantages & disadvantages
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
PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux
summary of the three approaches
defaults as implemented in SeaDAS
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 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
PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux
initial Rrs(670) measured by satellite (using Rrs(765) = 0)
bio-optical model to estimate Rrs(NIR)
PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux
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
bio-optical model to estimate Rrs(NIR)
PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux
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]
€
Rrs (670) =G(670)bb (670)
a(670) + bb (670)
bio-optical model to estimate Rrs(NIR)
PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux
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€
η=2.0 1−1.2 exp −0.9Rrs (443)Rrs (555)
⎛
⎝ ⎜
⎞
⎠ ⎟
⎡
⎣ ⎢
⎤
⎦ ⎥
bio-optical model to estimate Rrs(NIR)
PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux
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
€
bb (765) = bbw (765) + bbp (670)670765
⎛ ⎝ ⎜
⎞ ⎠ ⎟η
bio-optical model to estimate Rrs(NIR)
PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux
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)
€
Rrs (765) =G(765)bb (765)
aw (765) + bb (765)
aw(765) = 2.85 m-1
bio-optical model to estimate Rrs(NIR)
PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux
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)
bio-optical model to estimate Rrs(NIR)
PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux
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
bio-optical model to estimate Rrs(NIR)
PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux
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 Rrs(NIR)
PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux
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
PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux
comparisons with MERIS CoastColour
SeaWiFSMODIS-AquaMERISin situ
Middle Bay2005-2007
Rrs(l) 412-670
PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux
comparisons with MERIS CoastColour
SeaWiFSMODIS-AquaMERISin situ
Middle Bay2005-2007
derived productsChl, IOPs, Kd, TSM
PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux
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%
PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux
SNR transect for MODIS-Aqua NIR & SWIR bands
PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux
Aqua Chl “match-ups” for NIR & SWIR processing
PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux
MODIS-Aqua a(443)
PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux
distribution of the turbidity index using in NIR-SWIR
PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux
MODIS-Aqua vs. SeaWiFS
default processing ~ OC3 for MODIS-Aqua & OC4 for SeaWiFS
PJW, NASA, 13 Jun 2012, AtmCorr in Wimereux
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
atmospheric correction & the “black pixel” assumption