Transcript Introduction

A Bidirectional Reflectance Distribution Correction
Model for the Retrieval of Water Leaving Radiance
Data in Coastal Waters
Soe Hlaing*, Alex Gilerson, Samir Ahmed
Optical Remote Sensing Laboratory, NOAA-CREST
The City College of the City University of New York
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Bidirectional Reflectance Distribution Function
(BRDF)
Angular distribution in water leaving radiance field can typically vary 10 ~ 20%.
Generalized process to transform the water-leaving radiance measurements to the
hypothetical viewing geometry and solar position (usually at nadir viewing and solar
position) is called BRDF correction.
Especially important for satellite data validation and vicarious calibration
procedures.
Current operational BRDF correction algorithm [Morel et. al., 2002] is optimized for the
open ocean water conditions. Correction is based on the prior estimation of chlorophyll
concentration which is inappropriate for coastal waters.
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Bidirectional Reflectance Distribution Function
(BRDF)
Translate the remote-sensing reflectance into
Hypothetical Nadir Viewing
and
Solar Positions
Angular distribution in water leaving radiance field can typically vary 10 ~ 20%.
Generalized process to transform the water-leaving radiance measurements to the
hypothetical viewing geometry and solar position (usually at nadir viewing and solar
position) is called BRDF correction.
Especially important for satellite data validation and vicarious calibration
procedures.
Current operational BRDF correction algorithm [Morel et. al., 2002] is optimized for the
open ocean water conditions. Correction is based on the prior estimation of chlorophyll
concentration which is inappropriate for coastal waters.
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Why Case 2 optimized BRDF correction is needed?
Total Particulate Concentration for the Coastal and Open Ocean Waters
March 16 2010
Particulate Back-scattering bbp(443nm) (m -1)
0
0.05
0.1
0.15
 Inorganic non-algal particles are dominant constituents in coastal waters.
Current Operational Algorithm (from here on denoted as MG) Correction is based
on the prior estimation of chlorophyll concentration is inappropriate for coastal
waters. The need for the improved version of BRDF algorithm particularly tuned for
the typical coastal water conditions is general consensus among the ocean color
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remote-sensing community .
Contents
The Long Island Sound Coastal Observatory (LISCO).
Development of Case 2 water optimized CCNY BRDF
algorithm.
Assessments of the BRDF correction Algorithms:
oSimulated dataset
oin situ
osatellite Ocean Color data.
Conclusion
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Long Island Sound Coastal Observatory (LISCO)
MODIS AQUA true color composite image of Long Island Sound
(March 18 2010, 7:55 UTC)
New York
City
Long Island Sound Coastal Observatory (LISCO) is integral part of AERONET –
Ocean Color network (AERONET-OC) to support the Ocean Color data validation
activities through standardized products of normalized water-leaving radiance and
aerosol optical thickness.
LISCO is one of 15 operational AERONET-OC sites around the world.
LISCO is unique site in the world with collocated multi and hyperspectral
instrumentation for coastal waters monitoring.
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Features of the LISCO site
SeaPRISM instrument
Water Leaving
LISCO Tower
12 meters
Instrument Panel
Retractable Instrument
Tower
Solar Panel
Radiance (Lw)
Direct Sun
Radiance and Sky
Radiance (Li)
 Bands: 413, 443,
490, 551, 668, 870
and 1018 nm.
HyperSAS Instrument
Water Leaving
LISCO Platform
Co-located Multi- & Hyper-spectral
instruments for spectral band matching with
various current as well as future OC sensor.
Data acquisition every 30 minutes for
high time resolution time series
Radiance (Lw)
Sky Radiance (Li)
and Down Welling
Irradiance (Ed)
 Hyper-Spectral
305 to 900 nm
wavelength range.
 SeaPRISM takes 11 Lw & 3 Li measurements
 HyperSAS takes ~45 Lw & ~80 Li measurements
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Features of the LISCO site
Technical Differences between HyperSAS and SeaPRISM
Two Geometrical Configurations
HyperSAS
SeaPRISM
N
W
Instrument
Panel
Thanks to the rotation feature of SeaPRISM, its relative azimuth angle, φ, is always
set 90o with respect to the sun.
HyperSAS instrument is fixed pointing westward position all the time, thus φ is
changing throughout the day.
Both instruments point to the same direction when the sun is exactly at south.
This instrument setup provides the ideal configuration to make assessments of
the directional variation of the water leaving radiances.
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Development of Case 2 water
optimized CCNY BRDF algorithm
Bio-optical model and simulated datasets
Four Components
Bio-optical Model
Algal Particles [Chl]
Inherent Optical
Properties (IOP)
Non - Algal Particles [CNAP]
Absorption (a)
CDOM
Scattering (b, bb)
Remote-sensing Reflectance Rrs(λ) :
ratio between the water leaving
radiance Lw(λ) and down-welling
irradiance Ed(λ).
Rrs(λ) = Lw(λ) /Ed(λ)
Radiative
transfer
simulations
Pure Sea-water
(Hydrolight)
Generated as random variables in the
prescribe ranges typical for coastal
water conditions
[Chl] = 1 ~ 10mg/m3
CNAP = 0.01 ~ 2.5mg/m3
aCDOM = 0 ~ 2m-1
Particle Scattering Phase
Function Varied with
particle Concentration &
Composition
Remotesensing
Reflectance
Rrs(λ)
At all viewing & illumination
geometries
Viewing angle ( θv ) 0o ~ 80o
solar Zenith ( θs )
0o ~ 800
relative azimuth ( φ ) 0o ~ 180o
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Single back-scattering albedo (ω) vs. Rrs (λ) at
various illumination and viewing geometries
s = 0
s = 60
s = 60
0.02
 =45
0.015
0.01
0.005
bb

a  bb
0
0.1
0.2
 =90
0.01
0
0.3
0
0.1
0.2
s = 30
s = 60
s = 60
0.02
0.01
0.005
0.1
0.2
(412nm)
0
0.1
0.3
0.3
s = 0
0.025
s = 30
s = 60
0.02
 =90
0.015
0.01
0
0.2
(551nm)
0.005
0
0
0.3
s = 0
0.025
 =45
0.015
0.01
0.005
s = 30
Rrs(412nm) (Sr-1)
Rrs(412nm) (Sr-1)
0.02
 =180
0.015
(551nm)
s = 0
0.025
s = 60
0.02
0.015
(551nm)
0
s = 30
0.005
0
s = 0
0.025
Rrs(551nm) (Sr-1)
s = 30
Rrs(412nm) (Sr-1)
Rrs(551nm) (Sr-1)
0.02
s = 0
0.025
s = 30
Rrs(551nm) (Sr-1)
0.025
 =180
0.015
0.01
0.005
0
0.1
0.2
(412nm)
0.3
0
0
0.1
0.2
0.3
(412nm)
Well known strong relationship between the ω and Rrs [Gordon 1988, Lee 2004 & Park 2005 et.al].
ω ~ Rrs relationship also depends on the viewing and illumination geometries.
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Spectral dependency of the ω ~ Rrs relationship is also observed [Gilerson 2007 et.al].
New CCNY-BRDF correction algorithm
Optimized for typical Case-2 water conditions
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Rrs( s , v ,  ,  ,  )   i ( s , v ,  ,  ) ( )
i
i 1
θs
θv
φ
bb

a  bb
αi – Coefficients tabulated for sets of
θs – Solar zenith angle
θv – Viewing angle
φ – Solar-sensor relative azimuth angle
λ – Wavelengths
1. ω(λ) is calculated by fitting measured Rrs(θs, θv, φ, λ) to the model with αi(θs, θv, φ, λ)
BA
2. Then, Rrs0(λ) is calculated by plugging in ω(λ) in the model along with αi(0, 0, 0, λ)
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Assessments of the BRDF
correction Algorithms
Statistical Analysis of the Algorithms Based on Simulated
Dataset (1/2)
Rrs(v , s ,  )
CCNY
0
Rrsretrieved
(CCNY )
MG
0
Rrsretrieved
(MG )
Compare with
0
Rrsactual
AAPD
UPD
Standard Algorithm
CCNY Algorithm
100 N xi  yi
AAPD 

N i 1 xi
100 N xi  yi
UPD 

N i 1 xi
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Statistical Analysis of the Algorithms Based on Simulated
Dataset (2/2)
Rrs(v , s ,  )

CCNY
0
Rrsretrieved
(CCNY )
MG
0
Rrsretrieved
(MG )
100 xi  yi
xi
Compare with
0
Rrsactual

 Up to 26% in bi-directional variation is
observed addressing the need for the BRDF
correction.
 When corrected with MG algorithm, variation
is reduced.
 Nevertheless, 57% of the dataset have relative
percent difference more than 5% which is
Ocean Color Sensor community’s targeted
accuracy level
 This verifies the unsuitability of the Current
Algorithm optimized for the case 1 water
condition to be used for the optically complex
case 2 waters.
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Comparison between the Operational MG and Proposed
CCNY Algorithm with the LISCO Dataset
Before BRDF Correction
Corrected with MG
Corrected with CCNY
Current MG algorithm increases the dispersion and weaker correlation with R2
value 0.958.
The proposed CCNY algorithm shows significant improvement reducing the
dispersion between the two measurements
Spectral average absolute percent difference is reduced by 3.14% and
stronger correlation with R2 value 0.972
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Application to the Satellite Data
Corrected with MG
Corrected with CCNY
Wavelength (nm)
AAPD (%)
412
443
491
551
667
MG
46.43
38.85
16.68
13.61
24.54
CCNY
42.40
34.16
14.93
10.99
21.89
Improvement
4.03
4.69
1.75
2.62
2.65
The CCNY algorithm shows significant improvement over current MG algorithm reducing
the dispersion between the in-situ measurements and MODIS Aqua data.
Stronger correlation (0.926) is also observed with the CCNY processing.
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Spectral average absolute percent difference is improved by 3.14%.
Conclusion
 We proposed a new remote-sensing reflectance model
designed with the typical case-2 water conditions for
the BRDF correction.
 Significant improvements were observed with the
proposed algorithm for simulated, in-situ and satellite
dataset
 With the use of proposed algorithm, match-up
between the in-situ and OC sensors may be improved.
Better characterization of atmospheric correction
procedure is possible in OC-sensor validation.
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