The detection and measurement of temporal and spatial variability in biomass is important for studying large-scale coastal processes and dynamics including those controlling carbon cycling in the Great Lakes. There are a number of unknown factors, namely: (1) the unique sources of lake color variability in the Great Lakes; (2) the temporal and spatial variability of phytoplankton production; and (3) the effect of warmer temperatures on lower trophic food web dynamics. Ocean color sensors, such as SeaWiFS, have a unique role in understanding these processes because of the potential for estimating bio-optical parameters and production. The importance of this role depends on the ability of the sensors and algorithms to accurately discern and measure chlorophyll a concentrations, under varying optical conditions. The launch of SeaWiFS in 1997 resulted in the first ever daily [chl] estimates of the Great Lakes. However, standard NASA/SeaDAS processing produces negative, non-physical results in many scenes due to differences in the scale and optical properties of the Great Lakes. Evaluation of 12 marine bio-optical retrieval algorithms with in situ data indicates chlorophyll concentrations are overestimated by as much as 45:1 in the upper Great Lakes. Moreover, the areal extent of the Great Lakes, which is considerably smaller than other U.S. coastal regions, renders the standard NOAA and NASA 9 km~2 gridded level-3 products useless for detailed lake studies. Here, we provide time series of spatial and temporal variability in Great Lakes biomass and primary production, from October 1997 to September 2000.
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