in situ data in support of (atmospheric) ocean color satellite calibration & validation...
TRANSCRIPT
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In situ data in support of (atmospheric) ocean color satellite calibration &
validation activities
How are my data used? Part 2
Ocean Optics Class
University of Maine
July 2011
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ocean color satellite data products
primary data product: Lw(l)
secondary data products (those derived from Lw(l)):
AOPs: Rrs(l), Kd(l), PAR
IOPs: a(l), bb(l)
pigments: Chl, accessory pigmentscarbon stocks: DOC, POC, SPMphytoplankton/productivity: FLH, NPP, ϕ, PFTs,
PSCsetc.
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outline
(1)accounting for Rrs(NIR) < 0
(2)spectral bandpass adjustments
(3)BRDF considerations
all 3 modify the final satellite Rrs(l) using in situ data (thus, in situ data directly impact the fundamental climate data record)
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outline
(1)accounting for Rrs(NIR) < 0
(2)spectral bandpass adjustments
(3)BRDF considerations
![Page 5: In situ data in support of (atmospheric) ocean color satellite calibration & validation activities How are my data used? Part 2 Ocean Optics Class University](https://reader031.vdocuments.us/reader031/viewer/2022020417/5697c0301a28abf838cda880/html5/thumbnails/5.jpg)
challenges for remote sensing of coasts & estuaries
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
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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
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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
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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)
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are Rrs(NIR) really black?
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are Rrs(NIR) really black?
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what happens when we don’t account for Rrs(NIR) > 0?
use the “black pixel” assumption (e.g., SeaWiFS 1997-2000)
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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 6 (2009)
use a coupled ocean-atmosphere optimization e.g., Chomko & Gordon 2001, Stamnes et al. 2003, Kuchinke et al. 2009
what to do when Rrs(NIR) > 0?
field data!
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fixed aerosol & water contributions (MUMM)
assign & w(NIR) (via fixed values, a climatology, nearby pixels)
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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
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use of NIR + SWIR bands
use SWIR bands in “turbid” water, otherwise use NIR bands
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use of SWIR bands only
compare NIR & SWIR retrievals when considering only “turbid pixels”
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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
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correction of non-negligible Rrs(NIR)
estimate Rrs(NIR) using a bio-optical model
operational SeaWiFS & MODIS processing ~ 2000-present
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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
this is the method we discussed last Thu
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summary of the three approaches
defaults as implemented in SeaDAS
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outline
(1)accounting for Rrs(NIR) < 0
(2)spectral bandpass adjustments
(3)BRDF considerations
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satellite filter
in situ filter
spectral bandpass correction
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spectral bandpass correction
!
satellite filter
in situ filter
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why do we care?
satellite Rrs adjusted using a bio-optical model
relevant for satellite-to-in situ Rrs match-ups
spectral bandpass correction
field data!
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Morel & Maritorena 2001 (MM01) is an open ocean reflectance model for modeling Rrs(l) from Chl at ~ 1-nm resolution
it includes empirical relationships based on field data
spectral bandpass correction
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calculate Rrs(l) using MM01 for 0.01 < Chl < 3 mg m-3
spectral bandpass correction
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calculate Rrs(l) using MM01 for 0.01 < Chl < 3 mg m-3
for each satellite band li:for each Chlj:
calculate 10-nm mean Rrs(li,Chlj)calculate full-spectral-response Rrs(li,Chlj)ratio r(li,Chlj) = 10-nm / full-band
10-nmfull-bandratio
spectral bandpass correction
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calculate Rrs(l) using MM01 for 0.01 < Chl < 3 mg m-3
for each satellite band li:for each Chlj:
calculate 10-nm mean Rrs(li,Chlj)calculate full-spectral-response Rrs(li,Chlj)ratio r(li,Chlj) = 10-nm / full-band
10-nmfull-bandratio
spectral bandpass correction
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calculate Rrs(l) using MM01 for 0.01 < Chl < 3 mg m-3
for each satellite band li:for each Chlj:
calculate 10-nm mean Rrs(li,Chlj)calculate full-spectral-response Rrs(li,Chlj)ratio r(li,Chlj) = 10-nm / full-band
plot / regress ratio vs. full-band Rrs(490) / Rrs(555)derive polynomial expression to estimate ratio from full-band ratio
spectral bandpass correction
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r =
Rrs(1
0n
m)
/ R
rs(f
ull)
full-band Rrs(490) / Rrs(555)
spectral bandpass correction
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calculate Rrs(l) using MM01 for 0.01 < Chl < 3 mg m-3
for each satellite band li:for each Chlj:
calculate 10-nm mean Rrs(li,Chlj)calculate full-spectral-response Rrs(li,Chlj)ratio r(li,Chlj) = 10-nm / full-band
plot / regress ratio vs. full-band Rrs(490) / Rrs(555)derive polynomial expression to estimate ratio from full-band ratio
to “adjust” satellite full-band to 10-nm, apply correction factorsRrs(li,10-nm) = r(li) * Rrs(li,full-band)
spectral bandpass correction
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spectral bandpass correction
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spectral bandpass correction
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take care when executing satellite-to-in situ match-ups
when using multispectral in situ radiometers:enable the bandpass adjustment
when using hyperspectral in situ radiometers:enable the adjustment when applying 10-nm filter to in situ Rrs
enable the adjustment when applying full-spectral-response to in situ Rrs
spectral bandpass correction
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outline
(1)accounting for Rrs(NIR) < 0
(2)spectral bandpass adjustments
(3)BRDF considerations
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IN SITU
SUN
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IN SITU
SUN
SUN
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SUN
IN SITU
SUN
SUN
SATELLITE
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normalizing Rrs
account for Sun’s changing position in the sky:
pathlengths through atmosphere
transmission of light through air-sea & sea-air interfaces
angular features of in-water volume scattering functions
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normalizing Rrs
account for Sun’s changing position in the sky:
pathlengths through atmosphere
transmission of light through air-sea & sea-air interfaces
angular features of in-water volume scattering functions
normalize Rrs based on estimates of Chl or IOPsMorel, Voss, Lee - 1991 - present
field data!
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bb (l,CHL)• Loisel and Morel (1998)• empirical• dominated by oligotrophic observations• power law
Kbio (l,CHL) • Morel and Maritorena (2001)• empirical• dominated by oligotrophic observations• power law
VSF (l,CHL)• Morel et al. (2002)• semi-empirical (field data + Monte-Carlo)• uses bb model described above
the point being that
this is a Case 1 (Chl only) model
acknowledged by the authors
modeling IOPs from Chl
field data!
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Assume BRDF normalization model is correct. How much error introduced by using an erroneous input Chl value?
using estimates of Chl to adjust satellite Rrs
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variations in Level-2 satellite data, part 1
% difference
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variations in Level-2 satellite data, part 2
norm
/ u
nnorm
CH
L
sample
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… has going FOR it:
• a theoretical need for the additional normalization• convergence of SeaWiFS and MODIS-Aqua level-3 time series• few visual inconsistencies in level-2 files
… has going AGAINST it:
• (currently) limited Case 1 water model design, acknowledged by authors• operates via Rrs derived product (Chl,IOP) Rrs
• few level-2 analyses • difficult for community to verify models in the field• paucity of peer-reviewed options (until recently)
what normalizing Rrs using Chl …
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SNR transect for MODIS-Aqua NIR & SWIR bands
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MODIS-Aqua Level-2 Chl “match-ups” for NIR & SWIR processing
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MODIS-Aqua a(443)
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distribution of the turbidity index using in NIR-SWIR switching
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MARINE CONSITUENT ( f/Q ) BRDF CORRECTION
A brief history of the Morel model:
1991: study of how modifies subsurface reflectance, as a function of solar zenith angle (Morel and Gentili 1991)
1993: study of f, Q, and f/Q as a function of , with an emphasis on remote-sensing geometrical conditions (Morel and Gentili 1993)
1995: first field verification of Q (Morel, Voss, and Gentili 1995)
1996: study of f, Q, and f/Q as a function of CHL; introduction of normalization method and lookup tables for remote-sensing; introduction of Rgoth (Morel and Gentili 1996)
2002: update to 1996 work; new VSF’s and consideration of Ramanscattering; expansion of high CHL from 1 to 10 mg m-3 (Morel, Gentili, and Antoine 2002)
2004: second field verification of Q (Voss and Morel in press)
= ratio of molecular scattering to the total (molecular + particulate) scattering
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AIR
WATER
incident (water)
transmitted
reflected
na = 1.00
nw = 1.33
WATER-INCIDENT
MARINE CONSITUENT ( f/Q ) BRDF CORRECTION
light field modified by in-water constituents
defined by the following IOPs:
• bb (l,CHL)• Kbio (l,CHL) ~ proxy for a(l)• VSF (l,CHL)
Lwn = F Rgoth (f/Q) (bb/a)
• relate radiance to IOPs• both f & Q IOP-dependent
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VOLUME SCATTERING FUNCTION
0
medium
()
r
() = d() * d -1 * dr -1 * 0 -1
VSF = radiant flux per solid angleper unit path lengthexpressed as a portion of the incident flux
defines angular distribution of scattered flux
scattering coefficient, b = 4 () d
= water + particles
• water fixed• particles varies with CHL ( CHL bbp )• particulate scattering dominates at CHL > 1.0 mg m-3
• as of 2002, particles defined by 2 populations• relative role of small particles with CHL
backwardscattering
forwardscattering
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VOLUME SCATTERING FUNCTION
0
medium
()
r
backwardscattering
forwardscattering
Figure 3a from Morel et al. 2002
water
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one scene with this correction, one without