1. Soe Hlaing *, Alex Gilerson, Samir Ahmed Optical Remote Sensing Laboratory, NOAA-CREST The 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

2. 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. 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)

4.  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 4 Why Case 2 optimized BRDF correction is needed?

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

6.  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 6

7. SeaPRISM instrument  Water Leaving Radiance (L w )  Sky Radiance (L i ) and Down Welling Irradiance (E d )  Hyper-Spectral 305 to 900 nm wavelength range.  Water Leaving Radiance (L w )  Sky Radiance (L i ) and Down Welling Irradiance (E d )  Hyper-Spectral 305 to 900 nm wavelength range.  Water Leaving Radiance (L w )  Direct Sun Radiance and Sky Radiance (L i )  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 7 HyperSAS Instrument Features of the LISCO site 12 meters Retractable Instrument Tower Instrument Panel LISCO Tower Solar Panel LISCO Platform  SeaPRISM takes 11 L w & 3 L i measurements  HyperSAS takes ~45 L w & ~80 L i measurements

8. Instrument Panel SeaPRISMHyperSAS N W  Thanks to the rotation feature of SeaPRISM, its relative azimuth angle, φ, is always set 90 o 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. 8 Technical Differences between HyperSAS and SeaPRISM Two Geometrical Configurations Features of the LISCO site

9. Development of Case 2 water optimized CCNY BRDF algorithm

10. Bio-optical model and simulated datasets Remote- sensing Reflectance Rrs(λ) Inherent Optical Properties (IOP) Rrs(λ) = L w (λ) /E d (λ) Remote-sensing Reflectance Rrs(λ) : ratio between the water leaving radiance L w (λ) and down-welling irradiance E d (λ). 10 [Chl] = 1 ~ 10mg/m 3 C NAP = 0.01 ~ 2.5mg/m 3 a CDOM = 0 ~ 2m -1

11. Single back-scattering albedo (ω) vs. Rrs (λ) at various illumination and viewing geometries 11  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. BA θvθv φ θsθs New CCNY-BRDF correction algorithm Optimized for typical Case-2 water conditions α i – Coefficients tabulated for sets of θ s – Solar zenith angle θ v – Viewing angle φ – Solar-sensor relative azimuth angle λ – Wavelengths 12 1.ω(λ) is calculated by fitting measured Rrs(θ s, θ v, φ, λ) to the model with α i (θ s, θ v, φ, λ) 2.Then, Rrs 0 (λ) is calculated by plugging in ω(λ) in the model along with α i (0, 0, 0, λ)

13. Assessments of the BRDF correction Algorithms

14. Statistical Analysis of the Algorithms Based on Simulated Dataset (1/2) 14 Standard Algorithm CCNY Algorithm CCNY MG Compare with 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. CCNY MG Compare with 15

16. Comparison between the Operational MG and Proposed CCNY Algorithm with the LISCO Dataset  Current MG algorithm increases the dispersion and weaker correlation with R 2 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 R 2 value 0.972 16 Before BRDF Correction Corrected with MGCorrected with CCNY

17. Corrected with MGCorrected with CCNY Application to the Satellite Data 17 AAPD (%) Wavelength (nm) 412443491551667 MG 46.4338.8516.6813.6124.54 CCNY 42.4034.1614.9310.9921.89 Improvement 4.034.691.752.622.65  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. 18

19. 19 α1α1 α2α2 α3α3 θ v = 40 o λ = 443nm ( 1 st row ) & 551nm ( 2 nd row ) Remote sensing reflectance model for the correction of bidirectional reflectance distribution function  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 ].  λ : 412, 443, 491, 551, 668 & 753 nm  θ s : 0 to 80° at 10° intervals  θ v : 0 to 80° at 10° intervals  φ : 0 to 180° at 15° intervals * Totaling 6318 sets of α i coefficients.