Transcript Ocean Color Radiometer Measurements of Long Island Sound Coastal Observational platform (LISCO): Comparisons with Satellite Data & Assessments of Uncertainties

Ocean Color Radiometer Measurements of Long
Island Sound Coastal Observational platform (LISCO):
Comparisons with Satellite Data & Assessments of
Uncertainties
Presenter: Soe Min Hlaing
Mentor/ Co-Mentor: Dr. Samir Ahmed/ Dr. Alexander Gilerson
NOAA - CREST, City College of New York
Ocean Color
 Constituents of the water such as phytoplankton biomass can be estimated
through ocean color.
 Phytoplankton biomass is an important parameter in the studies of Global
Warming to regional ecological systems.
 Amount of phytoplankton in the ocean can be traced by the concentration of
the optically active pigment chlorophyll [Chl].
Global chlorophyll Concentration [Chl] Image
Remote Sensing of the Ocean Color
Absorption Spectrum of Water

Specific Absorption Spectrum of [Chl]
Absorption of Water is weakest in the visible part of the spectrum, so light
can penetrate down to 50 m in clear water.

Chlorophyll has optically active features in the visible region of the
spectrum.

Chlorophyll absorb strongly in the blue and violet/ ultra violet regions of the
spectrum
Reflectance spectra of the open ocean
0.04
[Chl]=0.2 mg/m3
[Chl]=1.0 mg/m3
Reflectance
0.03
[Chl]=10 mg/m3
0.02
0.01
0.00
400
500
600
700
800
Wavelength, nm
Remote Sensing Reflectance Spectra of
Ocean Water with Different Level of [Chl]
Chlorophyll Concentration [Chl] as function of
Blue – Green Ratio
 In the open ocean chlorophyll is the main constituent of the water.
 With increasing [Chl] water changes its color from blue to green.
 Therefore [Chl] can be well characterized by blue-green ratio.
 Blue-Green ratio algorithm does not work in coastal due to the
complication of color dissolved organic matters (CDOM)
Ocean Color Satellite Sensors
 SeaWIFS (NASA) on GeoEye's satellite
 8 spectral bands (from 412 to 865 nm) with 1.1 km resolution
 MODIS (NASA) on Terra and Aqua satellite
 36 spectral bands (from 412 to 15 μm) with 250m - 1km resolutions
 MERIS (ESA) on ENVISAT satellite
 16 spectral bands (from 412nm to 14.4 um) with 250m - 1km resolutions
 OCM2 (India) on Oceansat-2 satellite
 8 spectral bands (400 to 900nm) with 1 – 4 km resolutions
 VIIRS (NASA) future replacement of MODIS, planned to launch in 2011
 22 Spectral bands (370nm to 12.5 um) with 650m resolution
Contribution of atmospheric radiance to the
total signal
Atmospheric
Radiances
Sensor
Solar Flux
Surface Reflection
Diffuse Sky Light
Wavy Sea Surface
Water Leaving Radiances

Space-borne sensors view the sea through the atmosphere, thus atmospheric
perturbing effects, surface reflections, etc. are to be removed from the measured
total radiance.

Water leaving radiance accounts for only 10% of the total radiance.

Accurate retrieval of water leaving radiance depends on the atmospheric correction
process.
Validation of the Ocean Color Satellite Sensors
 Effectiveness of the water leaving radiance retrieval needs to be
accessed and validated.
 Special system for calibration and validation of the Ocean Color
satellites should be established.
 Objective of this work is to assess the capability of validation using
above water observations and evaluate the associated uncertainties
AERONET-Ocean Color
AERONET – Ocean Color:
is a sub-network of the Aerosol Robotic Network
(AERONET), relying on modified sun-photometers to support ocean color validation activities
with highly consistent time-series of water leaving radiance and aerosol optical thickness
measurements.
Rationale:
•Autonomous radiometers operated on fixed platforms in coastal regions;
•Identical measuring systems and protocols, calibrated using a single reference source and
method, and processed with the same code;
•Standardized products of normalized water-leaving radiance and aerosol optical thickness.
G.Zibordi et al. A Network for Standardized Ocean Color Validation Measurements. Eos Transactions, 87: 293, 297, 2006.
Long Island Sound Coastal
Observatory (LISCO)
MODIS Top of Atmosphere True Color Composite Image of Long Island Sound
LISCO Tower
Instrument Panel
12 meters
Retractable Instrument Tower
Solar panels
SeaPRISM and HyperSAS instruments
installed on the tower
SeaPRISM Instrument
HyperSAS Instrument
•Water Leaving Radiance
•Water Leaving Radiance
•Direct Sun Irradiance and Sky Radiance
•Sky Radiance and Down Welling Irradiance
• at 413, 443, 490, 551, 668, 870 and 1018nm
Wavelengths
• Hyper-Spectral 305 to 900 nm wavelength range.
Validation Procedure
Satellite Data
MODIS
SeaWIFS
In-Situ Data
MERIS
Cloud Free Level 2 Images
SeaPRISM
±40 minutes of Satellite Over Pass Time
Normalized Water Leaving Radiance (nLw)
412, 443, 488, 547 and 667nm
•Time Series Match Up and Comparison
•Relative and Absolute Percent Differences
•Correlation of the data at each wavelengths
Water Leaving Radiance Processing Procedure:
Removal of Sky Reflection
Li
o Total
radiance, LT , measured by water
viewing sensor is the sum of water leaving
radiance, Lw , the reflected component of the
sky radiance, Li and sun glint.
θ
o Sky reflection is proportional to Li with
reflectance factor, ρ.
o For a flat water surface ρ is just a constant
Fresnel reflectance factor.
LT = Lw+ρLi
Li
Lw
o Sun glint can be also minimized by arranging
the relative azimuth between the sensor and
sun.
Lw
o
Lw(λ) = LT (λ) - ρLi (λ)
π-θ
Water Leaving Radiance Processing Procedure:
Removal of Sky Reflection
o However, sea surface usually is wavy.
o ρ is a function of wind speed obtained through
simulations assuming Gausian Wave Slope
Li
o Glint components, L▼ becomes significant.
θ
LT = Lw+ρ(W)Li + L▼
Li
Lw
Simulation for the variability of sky
radiance direction with different wind speed
(Mobley, 1999)
π-θ
Bidirectional Reflectance Distribution Function (BRDF)
R0
f ( , a , W , IOP )  f ( ,  0 , a , W , IOP ) 

BRDF ( ,  0 ,  ,  , a , W , IOP ) 
 0
 
R( , W ) Q0 ( , a , W , IOP )  Q( ,  0 ,  ,  , a , W , IOP ) 
1
•Radiance emerging from the sea, in general, is not isotropic, it also depends on
the illumination and viewing conditions
•Viewing geometry dependency must be eliminated.
•Measurements are made at different times, so solar positions are not the same.
•Generalized process to transform the Lw measurements to the hypothetical
viewing geometry and solar position is called BRDF correction
Comparison of HyperSAS and SeaPRISM
measurements
Scatter plot of the comparison between HYPERSAS
and SeaPRISM datasets from October 2009 up to April 2010.
Time Series Normalized
Water Leaving
Radiance(nLw) Matchups
of SeaPRISM with satellite
data
Comparison of SeaPRISM and Satellite Data
Conclusions
•
Comparison between nLw data of SesPRISM and HyperSAS shows excellent
consistency.
•
Co-located instruments give us the quality assurance data to compare with the
satellite remote sensing data.
•
Comparison with the satellite data show excellent correlation at 488, 551 and 668 nm.
•
Initial assessments show relatively low Absolute Percent Difference through out the
spectrum.
•
Initial results proved the appropriateness of the LISCO site to achieve
calibration/validation of the ocean color satellites in coastal water area as a key
element of the AERONET-OC network
Acknowledgement
• This study was supported and monitored
by National Oceanic and Atmospheric
Administration (NOAA) under Grant CREST Grant # NA06OAR4810162.
• The statements contained within the
manuscript/research article are not the
opinions of the funding agency or the U.S.
government, but reflect the author’s
opinions.
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