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  • 1. 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
  • 2. 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 monitoring
            • Calibration-validation of ocean-color satellite data
    Water Body Above water radiometer
  • 3. Correction for Bidirectional Reflectance Distribution
    •  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-CORRECTION Algorithm
  • 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 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
  • 7. 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 )
  • 8. Rrs ( λ ) vs Single back-scattering albedo ( ω ) at various illumination and viewing geometries
    • 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:
    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.
  • 9. 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
    • CCNY algorithm in 2 steps:
    • (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 .
    • (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 )
    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
  • 10. Statistical Analysis/Comparison of the standard MG (Morel/Gentili) and proposed CCNY Algorithms Based on Simulated Dataset (1/2)
    • 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
    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
  • 11. 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%
    • 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
    CCNY algorithm Standard algorithm Without correction in %
  • 12. ASSESSMENT OF BRDF-CORRECTION APPLICATION TO ABOVE-WATER DATA AT LONG ISLAND SOUND COASTAL OBSERVATORY
  • 13.
    • 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 Multispectral SeaPRISM system as part of AERONET – Ocean Color network [Zibordi et al., 2006] LISCO Site Characteristics LISCO
  • 14. 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)
  • 15. LISCO Tower LISCO site Characteristics Platform : Collocated multispectral SeaPRISM and hyperspectral HyperSAS instrumentations since October 2009 12 meters Retractable Instrument Tower Instrument Panel
  • 16. SeaPRISM 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 Instrument Data acquisition every 30 minutes for high time resolution time series LISCO Instrumentation
  • 17. Instrument Panel Unique Capability of Making Near-Concurrent Water-Leaving Radiance From Different Viewing Geometries
    • Both instrument makes measurements with viewing angle, θ v = 40 o .
    • 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 ).
    • 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 .
    • LISCO site instrumentations configuration permits to assess accuracy of the bi-directionality correction of the water leaving radiances.
    Features of the LISCO site SeaPRISM HyperSAS N W
  • 18. 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
  • 19. 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)
    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 ]
  • 20. 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 R 2
    •  The proposed CCNY algorithm reduce dispersion by 2% in absolute value and by more than 3% in relative values
    Before BRDF Correction Corrected with MG Corrected with CCNY
  • 21. APPLICATION TO OCEAN COLOR MODIS IMAGERY
  • 22. 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,…)
  • 23. 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 ?
  • 24. Application to the Satellite Data
    •  Application 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
    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
  • 25. Conclusions
    • 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
  • 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)