R. Shuchman D. Pozdnyakov G. Leshkevich C. Hatt A. Korosov Altarum NIERSC NOAA GLERL

1. Verification and Application of aBio-optical Algorithm for Lake Michigan using SeaWiFS:a Seven-year Inter-annual Analysis Remote Sensing Across the Great Lakes: Observations, Monitoring and Action April 4-6, 2006, Rochester, NY R. Shuchman D. Pozdnyakov G. Leshkevich C. Hatt A. Korosov Altarum NIERSC NOAA GLERL

2. Outline • Water Quality Retrieval Algorithm Overview • Algorithm Validation • Example Results for Lake Michigan • Climate Change Modeling

3. Water Quality Retrieval Algorithm • Uses any visible spectrum sensing satellite • Detects spatial and temporal patterns in inland water bodies, including extreme and episodic events • Partnership between • Altarum Institute • Nansen International Environmental and Remote Sensing Centre (NIERSC) • NOAA GLERL • University of Michigan / Western Michigan University

4. Water Quality Retrieval Algorithm Retrievables: • Color Producing Agents (CPAs) • concentrations of phytoplankton chlorophyll (CHL) • suspended minerals (SM) • dissolved organic matter (DOC) Specific features: • Satellite- and water body-non-specific • Based on a hydro-optical model: Specific backscattering and absorption coefficients of CHL, SM and DOC • Combines Neural Networks with a Levenberg-Marquardt multivariate optimization procedure – the combination renders the algorithm computationally operational • Possesses quality assurance • Removal of pixels with poor atmospheric correction(SeaWiFS/MODIS standard procedures are applicable) • Removal of pixels that cannot be characterized by the hydro-optical model

5. Algorithm Flow Chart

6. Specific absorption coefficient for the ith water constituent Specific backscattering coefficient for the jth water constituent Remote Sensing Reflectance Measured Modeled Upwelling spectral radiance at the water surface Downwelling spectral irradiance at the water surface

7. The Levenberg-Marquardt Multivariate Optimization Procedure (1) = [ / ] measured reflectance at the wavelengthj (such as a measured from a satellite) reconstructed remote sensing reflectance, The residual between and can be computed by one of the following ways: The multidimensional least-square solution using all wavelengths is found by minimizing the squares of the residuals:

8. The Levenberg-Marquardt Multivariate Optimization Procedure (2) The absolute minimum of f(C) can be found with the Levenberg-Marquardt finite difference algorithm. An iteration procedure is initiated by creating an array of initial guess valuesC0.Each initial guess value is adjusted so that f(C) approaches a minimum. The value of C that provides the smallest f(C) can be determined to be the solution to the inverse problem. The number N of the initial vectors should not be excessively high because the computation time for the inverse problem solution increases proportionally with N. But the use of an array of initial vectors does not guarantee that the iterative procedure be converging, or/and the eventually established concentration vector be realistic. To help avoid this outcome and to speed up the algorithm, a priori limits are set based upon realistic concentration values.

9. Hydro-optical model • Used to reconstruct remote sensing reflectance from water parameters • Consists of a matrix of absorption and backscattering coefficients at each band wavelength for Chl, DOC and SM. • Initial HO model was based on Lake Ontario measurements from the 1980’s • Varies between different water bodies due to a difference in types of Chl, DOC and SM. Therefore, a hydro-optical model based upon one body of water may not be applicable to another.

10. The Algorithm Validation • Two shipborne campaigns: June and September 2003 • Historical data: 1998 – 2004 (GLERL, EEGLE)

11. Validation Data Collection • Satlantic Optical In Water Profiler

12. Sampling Sites in the Vicinity ofKalamazoo River Comparison of the chl concentrations (in g/l), obtained from in situ measurements (grey) and those retrieved from remote sensing data averaged over 9 neighboring pixels (black).

13. Sampling Sites in the Vicinity ofKalamazoo River Comparison of the doc concentrations (in mgC/L), obtained from in situ measurements (grey) and those retrieved from remote sensing data averaged over 9 neighboring pixels (black).

14. Sampling Sites in the Vicinity ofKalamazoo River Comparison of the sm concentrations (in mC/L), obtained from in situ measurements (grey) and those retrieved from remote sensing data averaged over 9 neighboring pixels (black).

15. Lake Michigan Characteristic Features • Dimictic lake (two overturns: the lake is vertically well mixed only from December to May) • Wind-driven circulation (coastal jets) • Episodic events: springtime resuspension (strong northerlies) and autumnal Ca precipitation (high water temperature Wind Driven Circulation

16. Seasonal Variations of Retrieved CPAs24 March 1998

17. Seasonal Variations of Retrieved CPAs17 April 1998

18. Seasonal Variations of Retrieved CPAs12 July 1998

19. Seasonal Variations of Retrieved CPAs25 August 1998

20. Seasonal Variations of Retrieved CPAs28 November 1998

21. Correlation Between Southern Lake Averaged sm and Northern Winds During Feb/March (r = 0.95) QuickSAT data 1.2 1.1 2002 1 2001 0.9 2003 0.8 sm concentration 0.7 2000 0.6 2004 0.5 0.4 0 2 4 6 8 10 12 number of days in February and March with strong northern winds

22. Correlation Between Southern Lake Averaged sm and Surface Temperature in August (r = 0.85) AVHRR Pathfinder data

23. Monthly Variation of Area Averaged sm and doc during Spring Episodic Event for 1998 in Southern Lake Michigan

24. Within along shoreline strip (Metric tons) Within off-shore outgrowth (Metric tons) Value for March 24, 2004 570,000 300,000 Mean March value 570,000 420,000 Spatial Distribution of (a) sm surface concentration, and (b)the sm Voluminal Content Per Square Kilometer

25. A Comparison of (a)the Spatial Distribution the sm Voluminal Content Per Square Kilometer, and (b) the Contours (in meters) of Bottom Sediment Accumulations Reported by Schwab et al.

26. A Comparison of Time Variations in doc and River Discharge for Grand River through 1998-2003

27. Climate Change Remote sensing in the visible as a companion tool for lake monitoring Climate Changes Lake change observations reaction from space scenario

28. Climate Change Scenarios for Lake Michigan: Major Ecological Consequences and Potential Identification from Space

29. Climate Change Scenarios for Lake Michigan: Major Ecological Consequences and Potential Identification from Space

30. Future Steps • The generation of specific hydro-optical models for each of the Great Lakes using radiometric data at the MODIS visible bands and coincident in situ measurements of color-producing agents. • Examining the temporal and spatial variations of the hydro-optical properties of Lake Erie. • The generation of a better atmospheric correction model for coastal regions in order to have more “usable” pixels in these areas. • The adaptation of the algorithm for use with hyper-spectral imagery from the Hyperion sensor, in order to obtain images of color-producing agents that are more accurate and have better (30 m) spatial resolution.

31. Further Information • Description of the Algorithm: Pozdnyakov, D., R. Shuchman, A. Korosov, and C. Hatt. 2005. Operational algorithm for the retrieval of water quality in the Great Lakes. Remote Sensing of Environment. 97: 353-370. • Application to Lake Michigan: Shuchman, R., A. Korosov, C. Hatt, D. Pozdnyakov, J. Means, and G. Meadows. 2005. Verification and Application of aBio-optical Algorithm for Lake Michigan using SeaWiFS:a Seven-year Interannual Analysis. Journal of Great Lakes Research. (in press, expected June 2006) • Contact Email: Robert.Shuchman@altarum.org