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![Page 1: The role of Optical Water Type classification in the context of GIOP Timothy S. Moore University of New Hampshire, Durham NH Mark D. Dowell Joint Research.](https://reader036.fdocuments.in/reader036/viewer/2022070414/5697c0251a28abf838cd5681/html5/thumbnails/1.jpg)
The role of Optical Water Type classification in the context of
GIOP
Timothy S. Moore University of New Hampshire, Durham NH
Mark D. DowellJoint Research Centre, Ispra Italy
September 25, 2010
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Rationale
• There is necessity to describe a considerable amount of variability in Inherent Optical Property (IOP) subcomponent models.
• This is particularly true, if inversion algorithms are to be applicable at global scale yet remain quantitatively accurate in coastal & shelf seas.
• This is unlikely to be achieved in the foreseeable future, with a single representation of IOP subcomponents.– BEAM – Case2R, GIOP
• The proposed approach is an algorithm framework more than a specific algorithm.
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Practical uses of a classification approach based on Optical Water Types (OWT)
• Describe variance and co-variance of optically active constituents
• Parameterizing IOP subcomponent models (or fit coefficients for empirical relationships)
• Selecting different inversions methods for different optical waters
• Avenue to spatial uncertainty estimates for remotely-sensed products
• Value-added products (directing new Cal/Val field work, data collection)
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Advantages of fuzzy logic defined provinces
• They allow for spatial and temporal dynamics both seasonal and inter-annual in the optical properties of a given region.
• They address the issue of transitions at the boundaries of provinces (through the fuzzy membership function of each class) thus resulting finally in the seamless reconstruction of a single geophysical product.
Our OWT method uses a fuzzy logic approach for optical classification of in situ and satellite data based on remote sensing reflectance.
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In-situ Database(NOMAD)Rrs()
IOPsSgd, aph*,…….
Station data sorted by class
Class based relationships
8 classes
Class Mi, Σi
Satellite Measurements
Individual classderived products
Merged Product
Calculatemembership
Rrs()
Conceptual Framework for class-based algorithms
Cluster Analysis
IOP model parameterization
IOP model/algorithm selection
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• 2407 data points (NOMAD v2)• 8 clusters ‘optimal’• representations of different optical water types (OWT)• mean and covariance matrix form the basis of the fuzzy membership function.
Base OWT Definition
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OWT 1 OWT 2 OWT 3 OWT 4
OWT 5 OWT 6 OWT 7 OWT 8
Mapping of the OWTs in ocean color data - example
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a() = aw() + Ac()[Chl]Bc() + [acdm(440)] exp(-Sdg(-440))
Possible coefficients to parameterize on an OWT-basis ina standard semi-analytic algorithm configuration.
bb() = bbw() +[bbp(555)] [555/]Y
Red - variables Yellow - parameters that need to be set (possible OWT dependency
a() = aw() + aph(Chl) + ad(TSS) + acdom(CDOM)
bb() = bbw() + bbp(Chl,TSS)
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Class–based GIOP Class–based QAA
• Sgd, Sg, Sd
• aph*()
• slope of bbp
• Sgd variable based on class• at(443) versus rrs(443)/rrs(555) class based• at(555) versus at(443) class based• aph(443) versus Chl class based aph*(443)
One could imagine applying a tuning algorithm (e.g. simulatedannealing) to each class to determine optimal
class based model coefficients.
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What follows is a look at the distribution and relationships of optical properties in the context of semi (quasi)-analytic algorithms from an OWT perspective based on the NOMAD v2 and IOCCG simulated data set.
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OWT NOMAD*
w/ IOPs
IOCCG
Sim.
Global Avg.
1 3 7 31
2 8 8 31
3 18 10 21
4 20 4 9
5 19 10 4
6 22 40 2
7 9 20 1
8 2 1 <1
Distribution of OWTs in Data sets vs. Ocean Obsverations (numbers are in percent)
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Sg v. ag443
OWT Sg
1 0.016
2 0.016
3 0.017
4 0.015
5 0.015
6 0.016
7 0.016
8 0.017
Avg. 0.016
Points are color coded by degree of membership to the OWT (based on Rrs).
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IOCCG
OWT 1
NOMAD
ag slope
ag 443
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ag440 -NOMAD
Sg
(Bricaud et al, 2009)
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aph*
log10 Chl
aph
OWT 1
OWT 2
OWT 4 OWT 8
OWT 7
OWT 6
OWT 5
OWT 3
OWT12345678
µ=7.87
µ=7.39
µ=3.22
µ=3.04
µ=0.148
µ=0.086
µ=0.331
µ=1.01
aph
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IOCCG
NOMAD
log ag443
OWT 1
log aph443 ag443/at443 bbp slope
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log ag443 bbp slopelog aph443
OWT 2
IOCCG
NOMAD
ag443/at443
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OWT 3
log ag443 bbp slopelog aph443
IOCCG
ag443/at443
NOMAD
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OWT 4
log ag443 bbp slopelog aph443
IOCCG
ag443/at443
NOMAD
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OWT 5
log ag443 bbp slopelog aph443
IOCCG
ag443/at443
NOMAD
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OWT 6
log ag443 bbp slopelog aph443
IOCCG
ag443/at443
NOMAD
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OWT 7
log ag443 bbp slopelog aph443
IOCCG
ag443/at443
NOMAD
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For what its worth…
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Sg bbp y aph 443 ag 443
OWT N I N I N I N I
1 0.016 0.0148 0.85 2.82 0.007 0.007 0.023 0.006
2 0.016 0.0146 1.74 2.62 0.013 0.010 0.026 0.012
3 0.017 0.0146 1.38 2.33 0.021 0.016 0.025 0.026
4 0.015 0.0151 1.03 1.99 0.048 0.029 0.056 0.052
5 0.015 0.0139 0.87 0.88 0.246 0.128 0.243 0.477
6 0.016 0.0148 0.88 0.62 0.277 0.154 0.289 0.483
7 0.016 0.0159 0.99 0.48 0.116 0.210 0.197 0.491
8 0.017 0.0154 - ~0 0.132 0.314 0.187 0.252
Avg 0.016 0.0149 1.03 1.21 0.135 0.118 0.149 0.329
Averages
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Chl
a ph*
Bricaud aph* function
Miscellaneous bio-optical empirical functions
OWT12345678
rrs443/rrs555
Y
(QAA)
1 50.5
2.5
2.0
1.5
1.0
0.5
0.0
OWT12345678
LAS Kd functionK
d443
rrs443/rrs555
rho
QAA
a555
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0
50
75
25
“Blue Hole”
Frequency of ‘low membership’ areas
100 %
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Summary
• There are some inconsistencies in the OWT-based distributions of IOPs between NOMAD and the IOCCG simulated data set.
• Both data sets are skewed towards coastal/case 2 waters.
• If a new simulated data set is being considered, the generation of IOPs and IOP pairs could be further constrained by the variance and co-variance as seen in NOMAD within different OWTs.
• In addition, the representation of data points could be guided by the global distribution of naturally occurring OWTs.
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Summary (continued)
• OWT code is currently in Seadas, but has yet to receive the final green light for public usage (we see no problem here).
• Preliminary OWT-based IOP parameters now exist and can be used in the GIOP framework.
• Potential for further use in parameterizing empirical models within GIOP is being explored.
• OWTs themselves may change over time, which could effect some of the OWT-based parameters (don’t think this to be major).
• Sensitivity and performance analysis remains to be assessed for GIOP-related products.
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log ag443
OWT 1
OWT 2
OWT 3
OWT 4
OWT 5
OWT 6
OWT 7
OWT 8 NOMAD ag443 OWTdistributions
ag41
1
OWT12345678
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ag440 -NOMAD
Sg
(Bricaud et al, 2009)
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Sd v. ad443
OWT Sd
1 0.011
2 0.011
3 0.010
4 0.010
5 0.011
6 0.011
7 0.012
8 0.011
Avg. 0.011
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Sdg v. adg443
OWT Sdg
1 0.014
2 0.014
3 0.015
4 0.013
5 0.013
6 0.013
7 0.014
8 0.014
Avg. 0.014
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OWT 5
aph*aph
• There are some issues with data quality that might be revealed.
Effects of aph to aph* conversion
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OWT 1
OWT 4
OWT 3
OWT 2
OWT 7
OWT 6
OWT 5b bp
OWT Y
1 0.85
2 1.74
3 1.38
4 1.03
5 0.87
6 0.88
7 0.99
8 -
Avg 1.03
bbp slope estimation
Y0 0.5 1 1.5 2.0
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Y
OWT Y
1 0.85
2 1.74
3 1.38
4 1.03
5 0.87
6 0.88
7 0.99
8 -
Avg.* 1.03
bbp slope estimation
* Negative values excluded
OWT 1
OWT 4
OWT 3
OWT 2
OWT 5
OWT 6
OWT 7
All