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
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Main topics

• Introduction
• definitions, sensor characteristics
• Model development
• IOPs, AOPs, Forward and Inversion approach
• Applications
• chl, phytoplankton size structure

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Ocean color sensors

• Definition
• Types
• Passive vs Active
• Sensor characteristics
• swath, footprint, revisiting time, spectral
  resolution

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• Atmospheric windows

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Ocean color sensors characteristics

• First sensors B W

• Spectral resolution
• number of channels?, bandwidth?

• Temporal resolution
• revisiting time?

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Ocean color sensors characteristics
http//www.ioccg.org/reports/
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Ocean color sensors characteristics
http//www.ioccg.org/reports/
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Ocean color sensors characteristics
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Ocean color sensors characteristics
Ideally we need to match channels and optical
signatures
SIO PIER
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Ocean color sensors characteristics
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Ocean color sensors Other criteria to keep in
mind
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Ocean color sensors S/N of detectors
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Ocean color sensors types
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Lidar 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

• Inherent and apparent Optical properties
• IOPS and biogeochemical parameters
• Forward vs Inversion models

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Inherent and Apparent Optical properties
IOPs not influenced by the light field (e.g.,
a, b, c coefficients)
IOPs influenced by the light field (e.g., Rrs,
Kd)
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IOPS biogeochemical parameters
VSF??
Absorption
Backscattering
Phytoplankton
CDOM
POC
SPM
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Forward vs Inversion models
Forward IOPs
Inversion Rrs
Rrs
IOPs
(Hydrolight or non-commercial code)
(Empirical, analytical, statistical)
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Forward vs Inversion models
Forward Monte Carlo simulations
Montes-Hugo et al. 2006, SPIE
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Inversion models
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Applications

• Chlorophyll a concentration in case II waters of
  Alaska
• Phytoplankton size structure in Antarctic waters

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Chlorophyll a concentration in case II waters of
Alaska

• Rrs Seawifs, MODIS, Microsas,
• hand-held spectrometer
• bb HydroScat
• Empirical band ratio vs
• spectral curvature

Montes-Hugo et al. 2005. RSE
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TOA
Remote sensing reflectance
200 m height
Spectral curvature
Validation
RMSlog10 0.41
RMSlog10 0.33
No regression
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STAY AWAY FROM CDOM USING LONGER WAVELENGTHS!!
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Phytoplankton size structure in Antarctic waters

• Spectral Backscattering approach
• bb from HS-6
• Rrs from PRR, SeaWiFS
• Phytoplankton size chl fractions , HPLC

bbx (?) M (?o/?) ?bbx
Montes-Hugo et al. 2007. IJRS
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Phytoplankton size structure in Antarctic waters
Field data
PRR
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Phytoplankton size structure in Antarctic waters
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HydroScat-6
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SeaWiFS
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Model validation based on HPLC signatures
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Thank you!!