1 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

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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

22

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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44

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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

66

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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?

99


http://www.ioccg.org/reports/

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http://www.ioccg.org/reports/

1111


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Ocean color sensors: characteristicsOcean color sensors: characteristicsIdeally we need to match channels and optical signatures

0

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: 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)

2020

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!!

Documents

1 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