Presenter: Soe Min Hlaing Mentor/ Co-Mentor: Dr. Samir Ahmed/ Dr. Alexander Gilerson

1. Ocean Color Radiometer Measurements of Long Island Sound Coastal Observational platform (LISCO): Comparisons with Satellite Data & Assessments of Uncertainties Presenter: Soe Min Hlaing Mentor/ Co-Mentor: Dr. Samir Ahmed/ Dr. Alexander Gilerson NOAA - CREST, City College of New York

2. Ocean Color • Constituents of the water such as phytoplankton biomass can be estimated through ocean color. • Phytoplankton biomass is an important parameter in the studies of Global Warming to regional ecological systems. • Amount of phytoplankton in the ocean can be traced by the concentration of the optically active pigment chlorophyll [Chl]. Global chlorophyll Concentration [Chl] Image

3. Remote Sensing of the Ocean Color Specific Absorption Spectrum of [Chl] Absorption Spectrum of Water • Absorption of Water is weakest in the visible part of the spectrum, so light can penetrate down to 50 m in clear water. • Chlorophyll has optically active features in the visible region of the spectrum. • Chlorophyll absorb strongly in the blue and violet/ ultra violet regions of the spectrum

4. Reflectance spectra of the open ocean Remote Sensing Reflectance Spectra of Ocean Water with Different Level of [Chl] Chlorophyll Concentration [Chl] as function of Blue – Green Ratio • In the open ocean chlorophyll is the main constituent of the water. • With increasing [Chl] water changes its color from blue to green. • Therefore [Chl] can be well characterized by blue-green ratio. • Blue-Green ratio algorithm does not work in coastal due to the complication of color dissolved organic matters (CDOM)

5. Ocean Color Satellite Sensors • SeaWIFS (NASA) on GeoEye's satellite • 8 spectral bands (from 412 to 865 nm) with 1.1 km resolution • MODIS (NASA) on Terra and Aqua satellite • 36 spectral bands (from 412 to 15 μm) with 250m - 1km resolutions • MERIS (ESA) on ENVISAT satellite • 16 spectral bands (from 412nm to 14.4 um) with 250m - 1km resolutions • OCM2 (India) on Oceansat-2 satellite • 8 spectral bands (400 to 900nm) with 1 – 4 km resolutions • VIIRS (NASA) future replacement of MODIS, planned to launch in 2011 • 22 Spectral bands (370nm to 12.5 um) with 650m resolution

6. Contribution of atmospheric radiance to the total signal Atmospheric Radiances Sensor Solar Flux Surface Reflection • Space-borne sensors view the sea through the atmosphere, thus atmospheric perturbing effects, surface reflections, etc. are to be removed from the measured total radiance. • Water leaving radiance accounts for only 10% of the total radiance. • Accurate retrieval of water leaving radiance depends on the atmospheric correction process. Diffuse Sky Light Wavy Sea Surface Water Leaving Radiances

7. Validation of the Ocean Color Satellite Sensors • Effectiveness of the water leaving radiance retrieval needs to be accessed and validated. • Special system for calibration and validation of the Ocean Color satellites should be established. • Objective of this work is to assess the capability of validation using above water observations and evaluate the associated uncertainties

8. AERONET-Ocean Color AERONET – Ocean Color: is a sub-network of the Aerosol Robotic Network (AERONET), relying on modified sun-photometers to support ocean color validation activities with highly consistent time-series of water leaving radiance and aerosol optical thickness measurements. Rationale: • Autonomous radiometers operated on fixed platforms in coastal regions; • Identical measuring systems and protocols, calibrated using a single reference source and method, and processed with the same code; • Standardized products of normalized water-leaving radiance and aerosol optical thickness. G.Zibordi et al. A Network for Standardized Ocean Color Validation Measurements. Eos Transactions, 87: 293, 297, 2006.

9. Long Island Sound Coastal Observatory (LISCO)

10. LISCO Tower Instrument Panel Retractable Instrument Tower 12 meters Solar panels

11. SeaPRISM and HyperSAS instruments installed on the tower • SeaPRISM Instrument • Water Leaving Radiance • Direct Sun Irradiance and Sky Radiance • at 413, 443, 490, 551, 668, 870 and 1018nm Wavelengths • HyperSAS Instrument • Water Leaving Radiance • Sky Radiance and Down Welling Irradiance • Hyper-Spectral 305 to 900 nm wavelength range.

12. Validation Procedure Satellite Data In-Situ Data SeaPRISM MODIS SeaWIFS MERIS Cloud Free Level 2 Images ±40 minutes of Satellite Over Pass Time Normalized Water Leaving Radiance (nLw) 412, 443, 488, 547 and 667nm • Time Series Match Up and Comparison • Relative and Absolute Percent Differences • Correlation of the data at each wavelengths

13. Water Leaving Radiance Processing Procedure:Removal of Sky Reflection Li • Total radiance, LT, measured by water viewing sensor is the sum of water leaving radiance, Lw, the reflected component of the sky radiance, Liand sun glint. • Sky reflection is proportional to Liwithreflectance factor, ρ. • For a flat water surface ρ is just a constant Fresnel reflectance factor. • Sun glint can be also minimized by arranging the relative azimuth between the sensor and sun. • Lw(λ) = LT (λ) - ρLi (λ) θ LT = Lw+ρLi Li π-θ Lw Lw

14. Water Leaving Radiance Processing Procedure:Removal of Sky Reflection • However, sea surface usually is wavy. • ρ is a function of wind speed obtained through simulations assuming Gausian Wave Slope • Glint components, L▼becomes significant. Li θ LT=Lw+ρ(W)Li+ L▼ Li π-θ Lw Simulation for the variability of sky radiance direction with different wind speed (Mobley, 1999)

15. Bidirectional Reflectance Distribution Function (BRDF) • Radiance emerging from the sea, in general, is not isotropic, it also depends on the illumination and viewing conditions • Viewing geometry dependency must be eliminated. • Measurements are made at different times, so solar positions are not the same. • Generalized process to transform the Lw measurements to the hypothetical viewing geometry and solar position is called BRDF correction

16. Comparison of HyperSAS and SeaPRISM measurements Scatter plot of the comparison between HYPERSAS and SeaPRISM datasets from October 2009 up to April 2010.

17. Time Series Normalized Water Leaving Radiance(nLw) Matchups of SeaPRISM with satellite data

18. Comparison of SeaPRISM and Satellite Data

19. Conclusions • Comparison between nLw data of SesPRISM and HyperSAS shows excellent consistency. • Co-located instruments give us the quality assurance data to compare with the satellite remote sensing data. • Comparison with the satellite data show excellent correlation at 488, 551 and 668 nm. • Initial assessments show relatively low Absolute Percent Difference through out the spectrum. • Initial results proved the appropriateness of the LISCO site to achieve calibration/validation of the ocean color satellites in coastal water area as a key element of the AERONET-OC network

20. Acknowledgement • This study was supported and monitored by National Oceanic and Atmospheric Administration (NOAA) under Grant - CREST Grant #  NA06OAR4810162. • The statements contained within the manuscript/research article are not the opinions of the funding agency or the U.S. government, but reflect the author’s opinions.

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