MO3.T10.5Ahmed.ppt

Alexander Gilerson1, Soe Hlaing1, Tristan Harmel1, Alberto Tonizzo1, Robert Arnone2, Alan Weidemann2, Samir Ahmed1

1Optical Remote Sensing Laboratory, City College, New York2 Naval Research Laboratory, Stennis Space Center

Bidirectional Reflectance Function in Coastal Waters And its Application to the Validation of the Ocean Color Satellites

1

Bidirectional Reflectance Distribution Function (BRDF)

• Radiance emerging from the sea, in general, is not isotropic.

• Varies directionally depending on viewing and illumination conditions.

• Bi-directionality property depends on Inherent Optical Properties (IOP) of the water constituents which are highly variable, especially in coastal environment

This bidirectional effect needs to be corrected to get standardized parameters suitable for :

Oceanic and Coastal waters monitoringCalibration-validation of ocean-color satellite data

2

Water Body

Above water radiometer

Correction for Bidirectional Reflectance Distribution

3

Adjust the remote-sensing reflectance for Hypothetical configuration of :

• Nadir Viewing • Sun at zenith

Current standard BRDF correction algorithm [Morel & Gentili 2002 et. al] :

Optimized for the open ocean water conditions. Correction is based on the prior estimation of chlorophyll concentration.

But, inappropriate for typical coastal waters usually dominated by sediment or by colored dissolved organic matters (CDOM)

4

BRDF-CORRECTIONAlgorithm

5

To analyze Case 2 BRDF, a dataset of remote sensing reflectances typical for coastal (Case 2) water conditions was generated through radiative transfer simulations for a large range of viewing and illumination geometries.

Based on this simulated dataset, a Case 2 water-focused remote sensing reflectance model is proposed to correct above-water and satellite water leaving radiance data for bidirectional effects.

Proposed model is validated with a one year time series of in situ above-water measurements acquired by collocated multi- and hyperspectral radiometers which have different viewing geometries.

With the use of proposed BRDF correction, match-up comparisons of in situ time series and the MODIS satellite data has been improved.

Outline

6

Theoretical Background

( , , , )( , , , , , ) ( , , )

( , , , , , )s

s v s vs v

f W IOPRrs W IOP W

Q W IOP

Fundamental equation which relates Rrs to optical properties [Morel 2002 et. al]:

merges reflection and refraction effects that occur when downward irradiance and upward radiance propagate through the air-water interface

f relates the magnitude of the irradiance reflectance just below the surface to IOP

Angular Coordinate Convention θv ~ Viewing angleθs ~ solar Zenith

φ ~ solar-sensor relative azimuth

BRDF correction:

( 0, , , )_ ( , ) ( , , ) ( )

( 0, 0, 0, , , )s

s vs v

f W IOPRrs corrected W IOP W

Q W IOP

Set f and Q for Sun at zenith and nadir view

Rrs(W,IOP)_corrected

Q= bidirectional function W = wind speed

ω = single back-scattering albedo ω = bb / ( a + bb ) determined by IOP

Bio-optical model and radiative transfer simulation

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)

Radiative transfer

simulations (Hydrolight)

Remote-sensing

Reflectance Rrs(λ)

Remote-sensing

Reflectance Rrs(λ)

1053 sets of Viewing & illumination geometries

Viewing angle ( θv ) 0o ~ 80o

solar Zenith ( θs ) 0o ~ 800

relative azimuth ( φ ) 0o ~ 180o

Wavelength:

412,443, 491, 551, 668 nm

Inherent Optical Properties (IOP)

7

Range of input parameters[Chl] = 1 to 10mg/m3

CNAP = 0.01 to 2.5mg/m3

aCDOM = 0 to 2m-1

ω = bb / ( a + bb ) can be directly

connected to Rrs through modeling500 sets of IOP

Obtain Rrs(λ) & equivalent ω(λ) from 500 sets of IOPs to investigate Rrs – ω relatioships for large sets of viewing and illumination geometries.

coefficients are generated for each set of viewing / illumination geometries as well as for each wavelength.These coefficients are applicable to typical coastal water conditions.

i

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)

Rrs

(412nm

) (S

r-1)

s = 0

s = 30

s = 60

=45

=45=90 =180

=90 =180

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

8

Rrs~f(ω) relationship also depends on the viewing and illumination geometries. Spectral dependency of the ω ~ Rrs relationship can be also observed [Gilerson 2007 et.al].

Rrs can be fitted to ω with a third order polynomial:

b

b

ba

b

Rrs ~ function(ω) with [Gordon 1988, Lee

2002 & Park 2005 ].

2 31 2 3( ) ( ) ( ) ( )Rrs

3

1

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

ivsivsRrs

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

ω – single backscattering albedo

θs – Solar zenith angle

θv – Viewing zenith angle

φ – Solar-sensor relative azimuth

λ – Wavelengths

9

CCNY algorithm in 2 steps:(1) From the measured Rrs(θs, θv, φ, λ): Solve and retrieve ω(λ) with the use of the least mean square fitting & tabulated αi (θs, θv, φ, λ) coefficients.

(2) Use the retrieved ω(λ ) in the equation with αi (θs=0, θv=0, φ=0, λ ) coefficients for nadir viewing and illumination to calculate the BRDF-corrected Rrs(θs=0, θv=0, φ=0, λ )

Tabulated

coefficients based on

radiative transfer

computation

Use of third order polynomial parameterization based on radiative transfer computation for large range of optical properties

generalized expression

Standard Algorithm

CCNY Algorithm

Statistical Analysis/Comparison of the standard MG (Morel/Gentili) and proposed CCNY Algorithms Based on Simulated Dataset (1/2)

10

y = 0.93*x – 8.4e-5 (Standard)

y = 1.00*x – 8.5e-6 (CCNY)

Regression lines

1

_ _100

_

Ni i

i i

Rrs actual Rrs retrievedAAPD

N Rrs actual

AAPD(Standard Algo)=9.5%

AAPD(CCNY Algo)=0.6%

Dispersion

The standard use of Case 1-water based BRDF MG correction induce almost 10% uncertainty in the remote sensing reflectance retrieval in typical coastal waters. The proposed algorithm permit to reduce this dispersion below 1% without adding any bias

CCNY algorithm

Standard algorithm

Without correction

_ _

_

Rrs actual Rrs retrieved

Rrs actual

in %

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

Up to 26% in bi-directional variation is observed addressing the need for a BRDF correction.

Standard MG algorithm: helps, but 57% of the dataset still have relative percent difference more than 5% which is the required accuracy for Ocean Color Sensor

CCNY algorithm: ~98% of the cases have relative percent difference less than 5%

11

Important need to incorporate Case-2 water based BRDF correction in the current data processing Possible suitability of CCNY-algorithm to fulfill the Ocean Color Radiometry requirements

12

ASSESSMENT OF BRDF-CORRECTION

APPLICATION TO ABOVE-WATER

DATAAT LONG ISLAND SOUND COASTAL

OBSERVATORY

Identical measuring systems and protocols, calibrated using a single reference source and method, and processed with the same code;

Standardized products of exact normalized water-leaving radiance and aerosol optical thickness

LISCO

13

LISCO Multispectral SeaPRISM system as part of AERONET – Ocean Color network

[Zibordi et al., 2006]

LISCO Site Characteristics

Water type: Moderately turbid and very productive (Aurin et al. 2010)

Bathymetry : plateau at 13 m depth

14

Location and BathymetryDepth in meters (GEBCO data)

LISCO Site Characteristics

12 m

eter

s

Retractable Instrument Tower

Instrument Panel

LISCO Tower

15

LISCO site Characteristics

Platform: Collocated multispectral SeaPRISM and hyperspectral HyperSAS instrumentations since October 2009

16

SeaPRISM instrumentSeaPRISM instrument

Sea Radiance

Direct Sun Radiance and Sky Radiance

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

Sea Radiance

Sky Radiance

Downwelling Irradiance

Linear Polarization measurements

Hyperspectral: 180 wavelengths [305,900] nm

HyperSAS InstrumentHyperSAS Instrument

Data acquisition every 30 minutes for high time resolution time series

LISCO Instrumentation

16

Instrument Panel

SeaPRISMHyperSAS

Unique Capability of Making Near-Concurrent Water-Leaving Radiance From Different Viewing Geometries

N

W

Both instrument makes measurements with viewing angle, θv = 40o.Thanks to the rotation feature of SeaPRISM, its relative azimuth angle, φ, is always set 90o with respect to the sun (resulting in water scattering angle range of 132 ~ 145o).HyperSAS instrument is fixed pointing westward position all the time, thus φ is changing throughout the day and resulting scattering angle range from 110 -175o.LISCO site instrumentations configuration permits to assess accuracy of the bi-directionality correction of the water leaving radiances.

Features of the LISCO site

17

Above Water Signal decomposition

Above-Water Data Processing

Li

Lw

θ

θ

LT = Lw + ρ(W) Li + Lg

Li

Sun

Total radiance Sky radiance

Water leaving radiance

Sea surface reflectivity

Sun glint radiance

18

Ed

Rrs = Lw /Ed

Down-welling Irradiance

Remote-sensing reflectance:

Needs to be corrected for the bidirectionality property

Comparison of SeaPRISM and HyperSAS

Increased dispersion in the right figure is mainly due to BRDF (filters exclude data from some geometries, specifically where relative azimuth angle, φ < 60° to eliminate

glint effects) 19

For all the viewing geometriesBoth instrument pointing same direction(within ±10° in Azimuth)

Rrs SeaPRISM [sr-1] Rrs SeaPRISM [sr-1]

Rrs

Hyp

erSA

S [s

r-1]

Rrs

Hyp

erSA

S [s

r-1]

Before BRDF Correction Corrected with MG Corrected with CCNY

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

Current MG algorithm does not reduce significantly the dispersion and induces a weaker correlation with R2

The proposed CCNY algorithm reduce dispersion by 2% in absolute value and by more than 3% in relative values

20

21

APPLICATION TO OCEAN COLOR MODIS IMAGERY

Satellite Validation

22

Satellite Pixel Selection for Matchup Comparison

3km×3km pixel box for matchup comparison

Exclusion of pixel box if presence of cloud-contaminated pixels in this 9km×9km pixel box

Validation of MODIS-Aqua against the LISCO DataSatellite Data Processing: Standard NASA Ocean Color Reprocessing 2009

Also exclusion of any pixel flagged by the NASA data quality check processing (Atmospheric correction failure, sun glint contamination,…)

Rrs Time series for the match-up comparison

Comparison between LISCO and MODIS Ocean Color data

Qualitative consistency in variations is observed between the in-situ and satellite data.

How will the Satellite / in situ data comparison be improved by application of the CCNY BRDF-correction ?

23

Corrected with Standard Algo Corrected with CCNY

Application to the Satellite Data

24

AAPD (%)Wavelength (nm)

412 443 491 551 667

Standard 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 2.65Application of the CCNY algorithm induces stronger correlation (0.926)Spectral average absolute percent difference is improved by more than 3%.

Suitability of CCNY BRDF-correction to significantly improve OCR satellite data accuracy in coastal areas

ConclusionsConclusions We proposed a new algorithm for BRDF correction of the remote-sensing

reflectance based on extensive radiative transfer calculations for typical coastal (case-2) waters conditions

Theoretical analysis showed that significant improvement are observed with the proposed algorithm reducing the uncertainty of this correction below 1%

This algorithm has been tested over the two years time series of LISCO observations. It has been shown that the CCNY BRDF-correction algorithm improve the accuracy of

the above-water data by more than 3%

Application of CCNY-algorithm to MODIS satellite data showed the same order of improvement. Suitability of CCNY BRDF-correction to significantly improve OCR satellite data accuracy in coastal areas

As a consequence of this work the operational application of this algorithm to current and future (VIIRS) OCR satellite is planned

25

26

ACKNOWLEDGMENTS

NASA AERONET team for SeaPRISM calibration, data processing and support of the site operations

NASA Ocean Color Processing Group for satellite imagery

Partial support from:Office of Naval Research (ONR)

National Oceanographic and Atmospheric Administration (NOAA)