Soe Hlaing*, Alex Gilerson, Samir Ahmed

Optical Remote Sensing Laboratory, NOAA-CRESTThe City College of the City University of New York

1

A Bidirectional Reflectance Distribution Correction Model for the Retrieval of Water Leaving Radiance

Data in Coastal Waters

Bidirectional Reflectance Distribution Function (BRDF)

2

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.

3

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.

Bidirectional Reflectance Distribution Function (BRDF)

Particulate Back-scattering bbp

(443nm) (m-1)

March 16 2010

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 remote-sensing community .

Total Particulate Concentration for the Coastal and Open Ocean Waters

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Why Case 2 optimized BRDF correction is needed?

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

Contents

5

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.

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

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SeaPRISM instrumentSeaPRISM instrument

Water Leaving Radiance (Lw)

Sky Radiance (Li) and Down Welling Irradiance (Ed)

Hyper-Spectral 305 to 900 nm wavelength range.

Water Leaving Radiance (Lw)

Sky Radiance (Li) and Down Welling Irradiance (Ed)

Hyper-Spectral 305 to 900 nm wavelength range.

Water Leaving Radiance (Lw)

Direct Sun Radiance and Sky Radiance (Li)

Bands: 413, 443, 490, 551, 668, 870 and 1018 nm.

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

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HyperSAS InstrumentHyperSAS Instrument

Features of the LISCO site12

met

ers

Retractable Instrument Tower

Instrument Panel

LISCO Tower

Solar Panel

LISCO Platform

SeaPRISM takes 11 Lw & 3 Li measurements

HyperSAS takes ~45 Lw & ~80 Li measurements

Instrument Panel

SeaPRISMHyperSAS

N

W

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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Technical Differences between HyperSAS and SeaPRISMTwo Geometrical Configurations

Features of the LISCO site

Development of Case 2 water optimized CCNY BRDF algorithm

Bio-optical model and simulated datasets

Remote-sensing

Reflectance Rrs(λ)

Generated as random variables in the prescribe ranges typical for coastal

water conditions

Particle Scattering Phase Function Varied with

particle Concentration & Composition

Radiative transfer

simulations (Hydrolight)

At all viewing & illumination geometries

Viewing angle ( θv ) 0o ~ 80o

solar Zenith ( θs ) 0o ~ 800

relative azimuth ( φ ) 0o ~ 180o

Inherent Optical Properties (IOP)

Rrs(λ) = Lw(λ) /Ed(λ)

Remote-sensing Reflectance Rrs(λ) : ratio between the water leaving

radiance Lw(λ) and down-welling irradiance Ed(λ).

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[Chl] = 1 ~ 10mg/m3

CNAP = 0.01 ~ 2.5mg/m3

aCDOM = 0 ~ 2m-1

0 0.1 0.2 0.30

0.005

0.01

0.015

0.02

0.025

(551nm)

Rrs

(551nm

) (S

r-1)

s = 0

s = 30

s = 60

0 0.1 0.2 0.30

0.005

0.01

0.015

0.02

0.025

(551nm)

Rrs

(551nm

) (S

r-1)

s = 0

s = 30

s = 60

0 0.1 0.2 0.30

0.005

0.01

0.015

0.02

0.025

(551nm)

Rrs

(551nm

) (S

r-1)

s = 0

s = 30

s = 60

0 0.1 0.2 0.30

0.005

0.01

0.015

0.02

0.025

(412nm)

Rrs

(412nm

) (S

r-1)

s = 0

s = 30

s = 60

0 0.1 0.2 0.30

0.005

0.01

0.015

0.02

0.025

(412nm)

Rrs

(412nm

) (S

r-1)

s = 0

s = 30

s = 60

0 0.1 0.2 0.30

0.005

0.01

0.015

0.02

0.025

(412nm)R

rs(4

12nm

) (S

r-1)

s = 0

s = 30

s = 60

=45

=45=90 =180

=90 =180

Single back-scattering albedo (ω) vs. Rrs (λ) at various illumination and viewing geometries

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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.Spectral dependency of the ω ~ Rrs relationship is also observed [Gilerson 2007 et.al].

b

b

ba

b

BA

θv

φ

θs

3

1

)(),,,(),,,,(i

ivsivsRrs

b

b

ba

b

New CCNY-BRDF correction algorithm Optimized for typical Case-2 water conditions

αi – Coefficients tabulated for sets of

θs – Solar zenith angle

θv – Viewing angle

φ – Solar-sensor relative azimuth angle

λ – Wavelengths

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1. ω(λ) is calculated by fitting measured Rrs(θs, θv, φ, λ) to the model with αi(θs, θv, φ, λ)

2. Then, Rrs0(λ) is calculated by plugging in ω(λ) in the model along with αi(0, 0, 0, λ)

Assessments of the BRDF correction Algorithms

Statistical Analysis of the Algorithms Based on Simulated Dataset (1/2)

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Standard Algorithm

CCNY Algorithm

N

i i

ii

x

yx

NAAPD

1

100

N

i i

ii

x

yx

NUPD

1

100

),,( svRrsCCNYMG

0)(CCNYretrievedRrs

0)(MGretrievedRrs

Compare with0

actualRrs

AAPD

UPD

i

ii

x

yx

100

Statistical Analysis of the Algorithms Based on Simulated Dataset (2/2)

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.

),,( svRrsCCNYMG

0)(CCNYretrievedRrs

0)(MGretrievedRrs

Compare with0

actualRrs

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Comparison between the Operational MG and Proposed CCNY Algorithm with the LISCO Dataset

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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Before BRDF Correction Corrected with MG Corrected with CCNY

Corrected with MG Corrected with CCNY

Application to the Satellite Data

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AAPD (%)

Wavelength (nm)

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 2.65The 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.Spectral average absolute percent difference is improved by 3.14%.

ConclusionWe 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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