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Enhancing Chlorophyll Retrievals in the Chesapeake Bay using Satellite Data Fusion

This study validates the GSM01 algorithm for chlorophyll products in the Chesapeake Bay region. The research focuses on removing atmospheric interference to extract light from the sea surface, relating spectral luminance to chlorophyll concentrations for phytoplankton assessment and bay health regulation through remote sensing techniques. Results show improved chlorophyll retrievals using multiple wavelengths and data fusion. Acknowledgements to collaborators at NASA and UNH for support.

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Enhancing Chlorophyll Retrievals in the Chesapeake Bay using Satellite Data Fusion

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  1. Validating the GSM01 algorithm for chlorophyll products in the Chesapeake Bay Mimi Szeto Research & Discover, GSFC Summer Intern 2007 Advisor: Jeremy Werdell, 614.2

  2. the satellite views the spectral light field at the top-of-the-atmosphere SATELLITE TOP-OF-THE-ATMOSPHERE 1. remove atmosphere from total signal to get light from sea surface (water-leaving radiance, Lw) 3. spatially / temporally bin and remap satellite Ca observations SEA SURFACE 2. relate spectral Lw to Ca (or geophysical product of interest) PHYTOPLANKTON Background: Remote Sensing

  3. Background: Why Chlorophyll? Photosynthesis in Chlorophyll Global Carbon Cycle Legal Action for Regulating Bay Health

  4. Background: How it works. Light Scattered Remote Sensing Equation Absorbed fcn Remote sensing reflectance, Derived from water leaving radiances Inherent Optical Properties

  5. Background: How it works. Inherent Optical Properties Remote Sensing Equation ABSORPTION fcn water plankton CDOM and non-algal particles SCATTERING water particles Inherent Optical Property = [magnitude] x (spectral shape) 2.5 1.0 0.008 2.0 0.8 0.006 S a*(l) 0.6 1.5 0.004 0.4 Absorption 1.0 0.002 0.2 0.5 0.0 0.000 0.0 600 400 500 600 700 400 500 700 400 500 600 700 Wavelengths Wavelengths Wavelengths

  6. Background: How it works. SeaWiFS Wavelengths  412 443  490  510 555   670  Chlorophyll concentration Model Parameters

  7. Background: In situ vs Satellite SATELLITE IN SITU nLw: Radiometer Chlorophyll: HPLC, fluorometer GSM01 nLw Chlorophyll • NOMAD • in situ data set (N: 2208, 281 in the Chesapeake Bay) • Chl Concentration, Rrs at SeaWiFS wavelengths • Modeled products vs In situ products

  8. GSM01

  9. Validation and Testing • Consolidate data from 3000 stations. • Statistically derive new model parameters ABSORPTION water plankton CDOM and non-algal particles SCATTERING water particles a*(l)& S Combo 1 Combo 2 Combo 3… GSM01 Modeled Chl 1 Modeled Chl 2 Modeled Chl 3 … NOMAD Rrs

  10. Results: Model Parameter GSM01CB a*(l) New Parameter a*(l)

  11. Results: Chlorophyll Modeled/Insitu Ratio (ACCURACY) (SPREAD)

  12. Validation and Testing • Extra tests • Sensitivity Analysis • Drop 412 • atmospheric problem • Drop 670 • often not reliable • Matrix Inversion • Backscattering Parameter GSM01 Negative Modeled Chlorophyll RESULTS

  13. Ten-Year Analyses • Distributions of % Frequency for 10 years of Chl data • GSM01NP - ineffective in Summer • High values • Better in Fall % Frequency Chl RED = IN SITU CHL BLACK = MODEL CHL % Frequency % Frequency Chl Chl

  14. Regional Blending BLENDING ALGORITHMS GSM01 GSM01NP SWITCH AT -75.7 W

  15. Conclusion • Validation was successful. • The new parameters help improve chlorophyll retrievals. • I created a method for sensitivity analysis. • Use all six wavelengths with the new parameters. • Blending algorithms can be used to avoid abrupt transitions in chlorophyll images

  16. Acknowledgements • Jeremy, Sean, Gene, Brian, and the rest of the Ocean Biological Processing Group at NASA • Research and Discover, UNH • Fellow interns

  17. Ten-Year Analyses Ten Year Time Series Comparison of Modeled Chlorophyll vs Insitu Chlorophyll

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