remote sensing applications in oceanography: how much we can see using ocean color?
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Remote sensing applications in Oceanography: How much we can see using ocean color?. Martin A Montes Ph.D Rutgers University Institute of Marine and Coastal Sciences. Spring 2008. Main topics. Introduction: definitions, sensor characteristics Model development: - PowerPoint PPT PresentationTRANSCRIPT
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Remote sensing applications in Oceanography:
How much we can see using ocean color?
Martin A Montes Ph.DRutgers University
Institute of Marine and Coastal Sciences
Spring 2008
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Main topics
Introduction:Introduction:definitions, sensor characteristicsdefinitions, sensor characteristics
Model development: Model development: IOP’s, AOP’s, Forward and Inversion approachIOP’s, AOP’s, Forward and Inversion approach
ApplicationsApplications: : chl, phytoplankton size structurechl, phytoplankton size structure
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Ocean color sensors
Definition:Definition:
Types:Types: Passive vs ActivePassive vs Active
Sensor characteristics:Sensor characteristics: swath, footprint, revisiting time, spectral resolutionswath, footprint, revisiting time, spectral resolution
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‘‘Atmospheric windows’Atmospheric windows’
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Ocean color sensors: characteristicsOcean color sensors: characteristics
•First sensors: B& W
•Temporal resolution:revisiting time?
•Spectral resolution: number of channels?, bandwidth?
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Ocean color sensors: characteristicsOcean color sensors: characteristics
http://www.ioccg.org/reports/
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Ocean color sensors: characteristicsOcean color sensors: characteristics
http://www.ioccg.org/reports/
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Ocean color sensors: characteristicsOcean color sensors: characteristics
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Ocean color sensors: characteristicsOcean color sensors: characteristicsIdeally we need to match channels and optical signatures
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0.001
0.002
0.003
0.004
0.005
0.006
0.007
0.008
300 400 500 600 700 800
Lambda (nm)
Rrs
(1/
sr)
..
SIO PIER
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Ocean color sensors: characteristicsOcean color sensors: characteristics
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Ocean color sensors: Ocean color sensors: Other criteria to keep in mindOther criteria to keep in mind
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Ocean color sensors: Ocean color sensors: S/N of detectorsS/N of detectors
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Ocean color sensors: typesOcean color sensors: types
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Lidar and detection of plankton and fish layersLidar and detection of plankton and fish layers
Spatial Variability in Spatial Variability in Biological Standing Stocks and SST across the GOA Basin and Shelves 2003. Evelyn Brown, Martin Montes, James Churnside. AFSC Symposium
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Model development Model development
Inherent and apparent Optical propertiesInherent and apparent Optical properties
IOP’S and biogeochemical parametersIOP’S and biogeochemical parameters
Forward vs Inversion modelsForward vs Inversion models
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Inherent and Apparent Optical Inherent and Apparent Optical propertiesproperties
IOP’s: not influenced by the light field (e.g., a, b, c coefficients)
IOP’s: influenced by the light field (e.g., Rrs, Kd)
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IOP’S & biogeochemical parametersIOP’S & biogeochemical parameters
Absorption Backscattering
Phytoplankton CDOM POC SPM
VSF??
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Forward vs Inversion modelsForward vs Inversion models
Forward:
IOP’s Rrs
(Hydrolight or non-commercial code)
Inversion:
Rrs
(Empirical, analytical, statistical)
IOP’s
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Forward vs Inversion modelsForward vs Inversion modelsForward: Monte Carlo simulations
Montes-Hugo et al. 2006, SPIE
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Inversion modelsInversion models
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ApplicationsApplications
1.1. Chlorophyll Chlorophyll aa concentration in case II concentration in case II waters of Alaskawaters of Alaska
2.2. Phytoplankton size structure in Phytoplankton size structure in Antarctic watersAntarctic waters
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Chlorophyll Chlorophyll aa concentration in case II waters of Alaska concentration in case II waters of Alaska
Montes-Hugo et al. 2005. RSE
•RRrsrs:: Seawifs, MODIS, Microsas, hand-held spectrometerbb = HydroScat
•Empirical:Empirical: band ratio vs spectral curvature
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TOA
200 m height
Spectral curvature
Remote sensing reflectance
RMSlog10 = 0.41
RMSlog10 = 0.33 No regression
Validation
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STAY AWAY FROM CDOM USING LONGER WAVELENGTHS!!
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Phytoplankton size structure in Antarctic watersPhytoplankton size structure in Antarctic waters
Montes-Hugo et al. 2007. IJRS
•Spectral Backscattering approach
•bb from HS-6
•Rrs from PRR, SeaWiFS
•Phytoplankton size: chl fractions , HPLC
bbx () = M (o/) bbx
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Phytoplankton size structure in Antarctic watersPhytoplankton size structure in Antarctic waters Field data PRR
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Phytoplankton size structure in Antarctic watersPhytoplankton size structure in Antarctic waters
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HydroScat-6HydroScat-6
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(a)
(b)
(a)
(b)
(c)
(d)
N
chl>20/chlT
0.0 0.4 0.8
F
0
10
20
CF
(%)
25
50
75
100
chl>20/chlT
0.0 0.4 0.8
F
0
10
20
CF
(%)
25
50
75
100
SeaWiFSSeaWiFS
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Model validation based on HPLC signaturesModel validation based on HPLC signatures
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Thank you!!