- Related Research Areas
- Carbon Cycle & Ecosystems
This proposal addresses the refinement of a SeaWiFS/MODIS algorithm developed by MTRI that retrieves Chlorophyll, Dissolved Organic Carbon, and Suspended Sediment estimates on a 1km-grid for the Laurentian Great Lakes, which comprise 20% of the earth's surface fresh water. The algorithm has been successfully used to generate a seven-year time-series of Lake Michigan Color Producing Agent values. These are calculated via a principal component analysis, which yields the physical quantities of CHL, DOC and SM, and will be updated using an improved hydro-optic model which will be generated using in situ optical properties obtained from each Great Lake during scientific cruises that have occurred during the last three years. An aspect of the algorithm refinement will include evaluating the use of a more specific atmospheric correction for SeaWiFS/MODIS data collected over the Great Lakes region. The improved algorithm will be validated using the extensive historical data on CHL, DOC, and SM that exists for the Great Lakes. Upon validation, time series (1998-present) of the CPA values will be generated for cloud free days using the combined SeaWiFs/MODIS data set. The CHL, DOC, and SM 1km-resolution maps in geospatial formats will be shared with the research community (MichiganView). The refined algorithm will be adapted on Hyperion and AVIRIS data. Finer-resolution estimates will merge with the MODIS estimates at river mouth along the west shore of Lake Michigan addressing spatial observation scales for water quality, information useful for determining future satellite system parameters. Ten-year time-series of the Great Lakes CPA estimates will be analyzed on an inter-annual time and varying spatial scale basis as part of this proposal. Monthly averages of each Great Lake will be generated, with additional sub-regions of lakes such as near river mouths separated out to enhance the interpretation of the CPA observations, particularly in watershed-based land-use/land-cover data.
Project PI: Robert Shuchman/Michigan Technological University
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