A model for determining satellite-derived primary productivity estimates for Lake Michigan
A new MODIS-based satellite algorithm to estimate primary production (PP) has been generated and evaluated for Lake Michigan. The Great Lakes Primary Productivity Model (GLPPM) is based on the work of Fee (1973) and Lang and Fahnenstiel (1996) but utilizes remotely sensed observations as input for model variables. The Color Producing Agent Algorithm (CPA-A) developed by Pozdnyakov et al. (2005) and Shuchman et al. (2006, in press 2013) is utilized to generate robust chlorophyll values and the NASA KD2M approach is used to obtain the diffuse attenuation coefficient (Kd). Only incident PAR and carbon fixation rates are additionally needed to generate the PP estimate. Comparisons of the satellite-derived PP estimates from single monthly images to average monthly field measurements made by NOAA/ GLERL found good agreement between estimates. Satellite derived PP estimates were used to calculate a preliminary Lake Michigan annual production of 8.5 Tg C/year. The GLPPM can be easily adapted to work on all the Great Lakes and therefore can be used to generate time series dating back to late 1997 (launch of SeaWiFS). These time series can contribute to improved assessment of Great Lakes primary productivity changes as a result of biological events, such as Dreissenid mussel invasions, climate change, and anthropogenic forcing.
IAGLR 56th Annual Conference on Great Lakes Research
Fahnenstiel, G. L.,
Sayers, M. J.,
Shuchman, R. A.,
A model for determining satellite-derived primary productivity estimates for Lake Michigan.
IAGLR 56th Annual Conference on Great Lakes Research,
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