With the exception of a few areas, Lake Michigan (LM) is an oligotrophic clear water body. It is predominantly in its littoral zone where ecology-relevant processes unfold due to a variety of natural and anthropogenic forcings arising from the watershed. However, the bottom influence there is strong enough to contaminate the at-satellite signal, thus impeding the remote sensing of water quality parameters within the coastal zone.

A new bio-optical retrieval algorithm, based on a forward radiation transfer model, LM specific hydro-optical model and the multivariate optimization technique is developed for operational retrieval from satellite data of water quality parameters in lakes optically shallow areas. The retrieval output encompasses the concentrations of major Color Producing Agents (CPAs), viz. phytoplankton chlorophyll, total suspended matter and yellow substance in transparent coastal waters with a variety of cover types: sand, silt, stands of Chara, and Cladophora, and limestone pebble.

The sensitivity of both forward and inverse models was tested for hydro-optical conditions inherent in LM. By means of forward simulations, the spectral signature variations of subsurface remote sensing reflectance, Rrswthe modifications of the upwelling signal (controlled by the bottom type and depth). It is shown that at very low concentrations of CPAs (less than 0.01 in respective unit) the optical influence of the bottom becomes indiscernible if the bottom depth, H approaches 20 m. In waters loaded with higher quantities of total suspended matter (TSM) and phytoplankton chlorophyll, CHL, the bottom influence ceases at H ~ 10 m.

The noise sensitivity has shown that the shallower the water column and higher bottom albedo the more significant in the ensuing error in CPA retrievals. E.g. for a sandy bottom and water column of 5 m, a 10% error in determining of albedo leads to a 18%, 28% and 10% error in retrieving, respectively, CHL, TSM and colored dissolved organic matter, CDOM.