NASA Approach to Vicarious Calibration · Radiometric Consistency of MERIS & SeaWiFS 412 443 490...
Transcript of NASA Approach to Vicarious Calibration · Radiometric Consistency of MERIS & SeaWiFS 412 443 490...
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NASA Approach to
Vicarious Calibration
IOCCG Vicarious Calibration Workshop
November 2013
Bryan Franz
and the
Ocean Biology
Processing Group
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we want to produce high quality data records of sufficient length,
consistency, and continuity to support climate and ecosystem research
PSO Anomaly
SeaWiFS MODISA NASA VIIRS MERIS
Multivariate Enso Index (MEI)
PSO Following
Berenfeld et al. 2006
Mean SST > 15C
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How do we achieve consistency?
• Focus on instrument calibration
– establishing temporal and spatial stability within each mission
• Apply common algorithms
– ensuring consistency of processing across missions
• Apply common vicarious calibration approach
– ensuring spectral and absolute consistency of water-leaving radiance
retrievals under idealized conditions
• Perform detailed trend analyses (hypothesis testing)
– assessing temporal stability & and mission-to-mission consistency
• Reprocess multi-mission timeseries
– incorporating new instrument knowledge and algorithm advancements
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Common Processing Approach
Multi-Sensor
Level-1 to Level-2
(common algorithms)
SeaWiFS L1A
MODISA L1B
MODIST L1B
OCTS L1A
MOS L1B
OSMI L1A
CZCS L1A
MERIS L1B
OCM-1 L1B
OCM-2 L1B
VIIRS-L1A/L1B
GOCI-L1B
HICO-L1B
Level-2 to Level-3
Level-2 Scene
observed
radiances
ancillary data
water-leaving
reflectances &
derived prods
Level-3 Global
Product
vicarious calibration
gain factors
predicted
at-sensor
radiances
in situ water-leaving
radiances (MOBY)
sensor-specific tables:
Rayleigh, aerosol, etc.
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NASA/OBPG Vicarious Calibration
band-specific “adjustment” factors that minimize mean bias between
in situ calibration source and satellite Rrs(l) retrievals.
system calibration
– compensates for error in both instrument calibration and retrieval algorithm
two step process
– calibrate NIR bands to improve aerosol type retrieval
– calibrate visible using calibrated aerosol retrieval and in situ radiometry
derived at top of atmosphere, fixed in space and time
– ratio of predicted TOA radiance to observed TOA radiance
– averaged over all match-ups
gi(l) =Ltpredicted
Ltobserved g(l) =
1
ngi(l)
i=1
n
å
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Lt(NIR) = Lother(NIR) + La(NIR) + Lw(NIR)
requires two assumptions:
• Lw in two NIR bands negligible (or known)
• calibration of one NIR band is perfect (e.g., g(865) = 1 for SeaWiFS)
calibration of remaining NIR band (e.g., 765 for SeaWiFS):
• using an assumed aerosol type, the associated model can be used in
combination with La(865) to predict La(765)
• operationally executed using a 15x15 pixel target in the South Pacific Gyre
(aerosol model r70f10v01; a = 0.685; based on Tahiti AERONET site)
• remains spatially/temporally independent of visible band calibration
known calculated
Vicarious Calibration of NIR
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in situ Lw(l)
gas pol glint whitecap air aerosol
Lt(l) = [ Lr(l) + La(l) + tLf(l) + TLg(l) + td(l)Lw(l) ] · tg(l) fp(l)
water
conversion for time and view
(0,,=0,=0) (0,,)
from satellite NIR bands
vicarious TOA radiance
Construction of predicted TOA radiance in visible
predicted
in practice, 5x5 pixel average
gi(l) =Ltpredicted
Ltobserved
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Rrs(l) = Lw(l) fb(l,0,0,0,Ca) / t0(l,0) cos(0) F0(l)
Conversion of in situ Lw to satellite Lw
fb(l,0,0,0,Ca) = (R0 f0/Q0) / (R f/Q)
Ca = f (Rrs(l))
0=0, =0,j=0 0, =0,j=0
t0(l,0) = exp[-teff/cos(0)] where teff = -ln[t0(l,0) cos(0)]
itera
tion
Lw(l) = Rrs(l) t0(l,0) cos(0) F0(l) / fb(l,0,,j,Ca)
given in situ Lw at satellite bandpass l with radiant path geometry
(0, =0, j=0), convert to satellite Lw and path geometry (0,,j).
brdf Sun
Morel at al. 2002
in situ Lw for satellite viewing geometry
satellite standard algorithm (OCx)
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MOBY Exclusion Criteria
Lw rms <= 5 %
Es rms <= 10 %
Es stability <= 10 %
Es diff <= 15 %
tilt and roll <= 5 degrees
where:
• Lw rms: The RMS of the percent error between Lw computed from the top 2
arms and Lw computed from all 3
• Es rms: The RMS of the percent error between Es and Ed(0+) (i.e. Es sensor
compared to Es extrapolated from Ed)
• Es stability: The percent error between the min and max measured Es (i.e.
we assess how much Es varies throughout the multiple Es measurements
that are interspersed between the lengthy Lu and Ed sampling cycle)
• TheoryEs diff: The RMS of the percent error between a modeled clear sky Es
and the measured Es (i.e. Es closest in time to the averaged Lu
measurement time)
• Wavelengths between 425 & 575 nm are used to evaluate these criteria.
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Example: SeaWiFS Vicarious Gains over Time
using MOBY and SPG
Franz, B.A., S.W. Bailey, P.J. Werdell, and C.R. McClain, F.S. (2007). Sensor-Independent Approach to
Vicarious Calibration of Satellite Ocean Color Radiometry, Appl. Opt., 46 (22).
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Cumulative mean vicarious gain
It requires many samples to reach a stable vicarious calibration, even in
clear (homogeneous) water with a well maintained instrument (MOBY)
SeaW
iFS
to M
OB
Y
Franz, B.A., S.W. Bailey, P.J. Werdell, and C.R. McClain, F.S. (2007). Sensor-Independent Approach to
Vicarious Calibration of Satellite Ocean Color Radiometry, Appl. Opt., 46 (22).
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NASA-derived Vicarious Gains
consistent processing algorithms and vicarious calibration methods and sources
SeaWiFS MODISA
VIIRS MERIS
NIR
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Radiometric (in)Consistency of MERIS & SeaWiFS
412
443
490
510
Deep-Water
solid line = SeaWiFS R2010.0
dashed = MERIS 3rd Reprocessing (ESA)
Rrs
(str
-1)
560 & 555
5-10% differences
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Radiometric Consistency of MERIS & SeaWiFS
412
443
490
510
Deep-Water
solid line = SeaWiFS R2010.0
dashed = MERIS R2012.1 (NASA)
Rrs
(str
-1)
560 & 555
670
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SeaWiFS
MERIS
oligotrophic
mesotrophic
eutrophic
Common Mission Mean Spectral Agreement
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MODISA
VIIRS
oligotrophic
mesotrophic
eutrophic
Common Mission Mean Spectral Agreement
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Alternative calibration sources, similar results
SeaWiFS Gains
Validation of Satellite Retrievals
Bailey, S.W., Hooker, S.B., Antoine, A., Franz, B.A., and Werdell, P.J. (2008). Sources and
assumptions for the vicarious calibration of ocean color satellite observations, Appl. Opt., 47 (12).
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Model-based Vicarious Calibration
Werdell, P.J., S.W. Bailey, B.A. Franz, A. Morel, and C.R. McClain (2007). On-orbit vicarious
calibration of ocean color sensors using an ocean surface reflectance model, Appl. Opt., 46 (23).
Bio-optical Model Morel and Maritorena, 2001.
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NASA-derived VIIRS Vicarious Gains
changes as mission progresses
Model-Based MOBY-R2012.1
MOBY R2013.1 MOBY R2013.0
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consistency of multi-mission time-series global mean mesotrophic waters
SeaWiFS MODISA NASA VIIRS MERIS
SeaWiFS MODISA NASA VIIRS MERIS
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Final Thoughts
• consistency in algorithms, calibration methods, and sources is required to
achieve consistency in the multi-mission data record
• we expect vicarious adjustment factors within a few %, otherwise we’re
doing something wrong in instrument calibration or algorithms
• typically, the standard deviation about the mean vicarious gain is ~1% in all
bands; uncertainty on the mean is assumed to decrease with samples size
• the most critical impact of vicarious calibration is to refine the spectral
dependence of the system, which drives most derived product algorithms
• the spectral dependence can be significantly refined in early mission
operations using alternative “truth” sources to get “in the ballpark”
• from the perspective of global change research, we just need one high
quality source with sufficient match-ups over the mission lifespan to achieve
a stable and accurate vicarious calibration (there is no rush)
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22