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Bidirectional Reflectance Function in Coastal Waters And its Application to the Validation of the Ocean Color Satellites Alexander Gilerson 1 , Soe Hlaing 1 , Tristan Harmel 1 , Alberto Tonizzo 1 , Robert Arnone 2 , Alan Weidemann 2 , Samir Ahmed 1 1 Optical Remote Sensing Laboratory, City College, New York 2 Naval Research Laboratory, Stennis Space Center

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Bidirectional Reflectance Distribution Function ( BRDF ) <ul><li>Radiance emerging from the sea, in general, is not isotropic . </li></ul><ul><li>Varies directionally depending on viewing and illumination conditions. </li></ul><ul><li>Bi-directionality property depends on Inherent Optical Properties ( IOP ) of the water constituents which are highly variable, especially in coastal environment </li></ul><ul><li> This bidirectional effect needs to be corrected to get standardized parameters suitable for : </li></ul><ul><ul><ul><ul><ul><li>Oceanic and Coastal waters monitoring </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>Calibration-validation of ocean-color satellite data </li></ul></ul></ul></ul></ul>Water Body Above water radiometer

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Correction for Bidirectional Reflectance Distribution <ul><li> Adjust the remote-sensing reflectance for </li></ul><ul><li>Hypothetical configuration of : </li></ul><ul><li>Nadir Viewing </li></ul><ul><li>Sun at zenith </li></ul><ul><li>Current standard BRDF correction algorithm [ Morel & Gentili 2002 et. al ] : </li></ul><ul><ul><ul><ul><ul><li>Optimized for the open ocean water conditions. </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>Correction is based on the prior estimation of chlorophyll concentration. </li></ul></ul></ul></ul></ul><ul><ul><li>But, inappropriate for typical coastal waters usually dominated by sediment or by colored dissolved organic matters (CDOM) </li></ul></ul>

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BRDF-CORRECTION Algorithm

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

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Theoretical Background 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 θ v ~ Viewing angle θ s ~ solar Zenith φ ~ solar-sensor relative azimuth BRDF correction: 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 = b b / ( a + b b )  determined by IOP

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Bio-optical model and radiative transfer simulation 1053 sets of Viewing & illumination geometries Viewing angle ( θ v ) 0 o ~ 80 o solar Zenith ( θ s ) 0 o ~ 80 0 relative azimuth ( φ ) 0 o ~ 180 o Wavelength: 412,443, 491, 551, 668 nm Inherent Optical Properties (IOP) Range of input parameters [Chl] = 1 to 10mg/m 3 C NAP = 0.01 to 2.5mg/m 3 a CDOM = 0 to 2m -1 ω = b b / ( a + b b ) can be directly connected to Rrs through modeling 500 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. 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) Remote-sensing Reflectance Rrs ( λ )

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Rrs ( λ ) vs Single back-scattering albedo ( ω ) at various illumination and viewing geometries <ul><li>Rrs~f( ω ) relationship also depends on the viewing and illumination geometries. </li></ul><ul><li>Spectral dependency of the ω ~ Rrs relationship can be also observed [ Gilerson 2007 et.al ]. </li></ul><ul><li> Rrs can be fitted to ω with a third order polynomial: </li></ul>Rrs ~ function( ω ) with [ Gordon 1988, Lee 2002 & Park 2005 ]. 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.

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CCNY-BRDF correction algorithm Optimized for typical Case-2 water conditions ω – single backscattering albedo θ s – Solar zenith angle θ v – Viewing zenith angle φ – Solar-sensor relative azimuth λ – Wavelengths <ul><li>CCNY algorithm in 2 steps: </li></ul><ul><li>(1) From the measured Rrs ( θ s , θ v , φ , λ): S olve and retrieve ω (λ) with the use of the least mean square fitting & tabulated α i ( θ s , θ v , φ , λ) coefficients . </li></ul><ul><li>(2) Use the retrieved ω (λ ) in the equation with α i ( θ s =0, θ v =0, φ =0, λ ) coefficients for nadir viewing and illumination t o calculate the BRDF-corrected Rrs ( θ s =0, θ v =0, φ =0, λ ) </li></ul>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

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Statistical Analysis/Comparison of the standard MG (Morel/Gentili) and proposed CCNY Algorithms Based on Simulated Dataset (1/2) <ul><li>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. </li></ul><ul><li>The proposed algorithm permit to reduce this dispersion below 1% without adding any bias </li></ul>Standard Algorithm CCNY Algorithm y = 0.93* x – 8.4e -5 (Standard) y = 1.00* x – 8.5e -6 (CCNY) Regression lines AAPD(Standard Algo)=9.5% AAPD(CCNY Algo)=0.6% Dispersion

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Statistical Analysis of the Algorithms Based on Simulated Dataset (2/2) <ul><li>Up to 26% in bi-directional variation is observed addressing the need for a BRDF correction. </li></ul><ul><li>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 </li></ul><ul><li>CCNY algorithm: ~98% of the cases have relative percent difference less than 5% </li></ul><ul><li>Important need to incorporate Case-2 water based BRDF correction in the current data processing </li></ul><ul><li>Possible suitability of CCNY-algorithm to fulfill the Ocean Color Radiometry requirements </li></ul>CCNY algorithm Standard algorithm Without correction in %

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ASSESSMENT OF BRDF-CORRECTION APPLICATION TO ABOVE-WATER DATA AT LONG ISLAND SOUND COASTAL OBSERVATORY

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<ul><li>Identical measuring systems and protocols, calibrated using a single reference source and method, and processed with the same code; </li></ul><ul><li> Standardized products of exact normalized water-leaving radiance and aerosol optical thickness </li></ul>LISCO Multispectral SeaPRISM system as part of AERONET – Ocean Color network [Zibordi et al., 2006] LISCO Site Characteristics LISCO

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Water type: Moderately turbid and very productive (Aurin et al. 2010) Bathymetry : plateau at 13 m depth Location and Bathymetry LISCO Site Characteristics Depth in meters (GEBCO data)

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LISCO Tower LISCO site Characteristics Platform : Collocated multispectral SeaPRISM and hyperspectral HyperSAS instrumentations since October 2009 12 meters Retractable Instrument Tower Instrument Panel

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SeaPRISM instrument <ul><li>Sea Radiance </li></ul><ul><li>Direct Sun Radiance and Sky Radiance </li></ul><ul><li>Bands: 413, 443, 490, 551, 668, 870 and 1018 nm </li></ul><ul><li>Sea Radiance </li></ul><ul><li>Sky Radiance </li></ul><ul><li>Downwelling Irradiance </li></ul><ul><li>Linear Polarization measurements </li></ul><ul><li>Hyperspectral: 180 wavelengths [305,900] nm </li></ul>HyperSAS Instrument Data acquisition every 30 minutes for high time resolution time series LISCO Instrumentation

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Instrument Panel Unique Capability of Making Near-Concurrent Water-Leaving Radiance From Different Viewing Geometries <ul><li>Both instrument makes measurements with viewing angle, θ v = 40 o . </li></ul><ul><li>Thanks to the rotation feature of SeaPRISM , its relative azimuth angle, φ , is always set 90 o with respect to the sun (resulting in water scattering angle range of 132 ~ 145 o ). </li></ul><ul><li>HyperSAS instrument is fixed pointing westward position all the time, thus φ is changing throughout the day and resulting scattering angle range from 110 -175 o . </li></ul><ul><li>LISCO site instrumentations configuration permits to assess accuracy of the bi-directionality correction of the water leaving radiances. </li></ul>Features of the LISCO site SeaPRISM HyperSAS N W

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Above Water Signal decomposition Above-Water Data Processing Sun T otal radiance Sky radiance Water leaving radiance Sea surface reflectivity Sun glint radiance E d Rrs = L w / E d Down-welling Irradiance Remote-sensing reflectance: Needs to be corrected for the bidirectionality property L i L w θ θ L T = L w + ρ (W) L i + L g L i

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Comparison of SeaPRISM and HyperSAS <ul><li>Increased dispersion in the right figure is mainly due to BRDF </li></ul><ul><li>(filters exclude data from some geometries, specifically where relative azimuth angle, φ < 60° to eliminate glint effects) </li></ul>For all the viewing geometries Both instrument pointing same direction (within ±10° in Azimuth) Rrs SeaPRISM [sr -1 ] Rrs SeaPRISM [sr -1 ] Rrs HyperSAS [sr -1 ] Rrs HyperSAS [sr -1 ]

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Comparison between the Standard MG and Proposed CCNY Algorithm with the LISCO Dataset <ul><li>Current MG algorithm does not reduce significantly the dispersion and induces a weaker correlation with R 2 </li></ul><ul><li> The proposed CCNY algorithm reduce dispersion by 2% in absolute value and by more than 3% in relative values </li></ul>Before BRDF Correction Corrected with MG Corrected with CCNY

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APPLICATION TO OCEAN COLOR MODIS IMAGERY

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Satellite Validation 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 Data Satellite 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,…)

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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 ?

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Application to the Satellite Data <ul><li> Application of the CCNY algorithm induces stronger correlation (0.926) </li></ul><ul><li>Spectral average absolute percent difference is improved by more than 3%. </li></ul><ul><li>Suitability of CCNY BRDF-correction to significantly improve OCR satellite data accuracy in coastal areas </li></ul>Corrected with Standard Algo Corrected with CCNY 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 2.65

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Conclusions <ul><li>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 </li></ul><ul><li>Theoretical analysis showed that significant improvement are observed with the proposed algorithm reducing the uncertainty of this correction below 1% </li></ul><ul><li>This algorithm has been tested over the two years time series of LISCO observations. </li></ul><ul><ul><li>It has been shown that the CCNY BRDF-correction algorithm improve the accuracy of the above-water data by more than 3% </li></ul></ul><ul><li>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 </li></ul><ul><li>As a consequence of this work the operational application of this algorithm to current and future (VIIRS) OCR satellite is planned </li></ul>

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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)