Optimized BRDF Correction Model for Coastal Water Radiance Retrieval

1. 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

2. 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.

3. 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.

4. Why Case 2 optimized BRDF correction is needed? • Total Particulate Concentration for the Coastal and Open Ocean Waters • 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 .

5. Contents • The Long Island Sound Coastal Observatory (LISCO). • Development of Case 2 water optimized CCNY BRDFalgorithm. • Assessments of the BRDF correction Algorithms: • Simulated dataset • in situ • satellite Ocean Color data. • Conclusion

6. 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 integralpart 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.

7. Features of the LISCO site SeaPRISM instrument LISCO Tower • Water Leaving Radiance (Lw) • Direct Sun Radiance and Sky Radiance (Li) • Bands: 413, 443, 490, 551, 668, 870 and 1018 nm. Instrument Panel Retractable Instrument Tower HyperSAS Instrument • Water Leaving Radiance (Lw) • Sky Radiance (Li) and Down Welling Irradiance (Ed) • Hyper-Spectral 305 to 900 nm wavelength range. 12 meters Solar Panel 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 • SeaPRISM takes 11 Lw& 3 Limeasurements • HyperSAS takes ~45 Lw& ~80 Limeasurements

8. Features of the LISCO site Technical Differences between HyperSAS and SeaPRISM Two Geometrical Configurations N Instrument Panel 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. HyperSAS SeaPRISM

9. Development of Case 2 water optimized CCNYBRDFalgorithm

10. Bio-optical model and simulated datasets Remote-sensing Reflectance Rrs(λ): ratio between the water leaving radiance Lw(λ)and down-welling irradiance Ed(λ). Inherent Optical Properties (IOP) Four Components Bio-optical Model Rrs(λ) = Lw(λ) /Ed(λ) Absorption (a) Algal Particles [Chl] Remote-sensing Reflectance Rrs(λ) Scattering (b, bb) Non - Algal Particles [CNAP] Radiative transfer simulations (Hydrolight) CDOM Pure Sea-water [Chl] = 1 ~ 10mg/m3 CNAP = 0.01 ~ 2.5mg/m3 aCDOM = 0 ~ 2m-1 Particle Scattering Phase Function Varied with particle Concentration & Composition Generated as random variables in the prescribe ranges typical for coastal water conditions At all viewing & illumination geometries Viewing angle ( θv)0o ~ 80o solar Zenith ( θs)0o ~ 800 relative azimuth ( φ )0o ~ 180o

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

12. New CCNY-BRDF correction algorithm Optimized for typical Case-2 water conditions θs αi – Coefficients tabulated for sets of θs – Solar zenith angle θv – Viewing angle φ – Solar-sensor relative azimuth angle λ – Wavelengths θv φ BA ω(λ) is calculated by fitting measured Rrs(θs, θv, φ, λ) to the model with αi(θs, θv, φ, λ) Then, Rrs0(λ) is calculated by plugging in ω(λ) in the model along with αi(0, 0, 0, λ)

13. Assessments of the BRDF correction Algorithms

14. Statistical Analysis of the Algorithms Based on Simulated Dataset (1/2) Standard Algorithm CCNY Algorithm MG Compare with CCNY AAPD UPD

15. 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. MG Compare with CCNY

16. 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

17. Application to the Satellite Data Corrected with MG Corrected with CCNY • 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. • Spectral average absolute percent difference is improved by 3.14%.

18. 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.