Creating a representative Lake Erie time series of remote sensing-based water quality data sets
Remote sensing provides a method to accurately assess water quality for current and historical conditions in large lakes such as Lake Erie. Satellite sensors such as SeaWiFS and MODIS collect data that span large geographic areas and have been in operation for more than a decade, with some satellite programs that have existed since the 1970s. This remote sensing "time machine" allows scientist to analyze a time series of data and determine how water quality conditions have changed, particularly in light of a changing climate. Augmentation of remotely sensed data with sound in-situ measurements allows scientists to gain a deeper understanding of changes in the Great Lakes. This presentation reviews a recent collaborative study between the Michigan Tech Research Institute and the NASA Glenn Research Center that detailed satellitederived products to assess changes in Lake Erie water quality, described ancillary observations to support the time series analysis, and derived the representative set of products to characterize Lake Erie's water quality and characteristics. Remote sensing analysis outputs included retrieving color-producing agent (CPA) products (chlorophyll, suspended minerals, and dissolved organic carbon concentrations), sediment plume extents, optical water parameters, and harmful algal bloom extents.
IAGLR 56th Annual Conference on Great Lakes Research
Brooks, C. N.,
Shuchman, R. A.,
Grimm, A. G.,
Sayers, M. J.,
Raymer, Z. B.,
Jessee, N. L.,
Banach, D. M.
Creating a representative Lake Erie time series of remote sensing-based water quality data sets.
IAGLR 56th Annual Conference on Great Lakes Research,
Retrieved from: http://digitalcommons.mtu.edu/mtri_p/96