An Operational Algorithm for the Retrieval of Water Quality Parameters in the Great Lakes from Satellite Data

Water quality parameters provide unique observations of the lower food web, including primary production, to help better understand ecological changes due to anthropogenic forcing and climate change. The Color Producing Agent Algorithm (CPA-A) is a semi-analytical inverse radiative transfer bio-optical model to retrieve water quality parameters from satellite observed reflectance. The CPA-A requires knowledge of the inherent optical properties of a given water body to produce accurate retrievals of the primary color producing agents (CPAs) namely chlorophyll (CHL), suspended mineral (SM), and CDOM. An all season, multi-year measured set of inherent optical properties with concurrent CPA concentrations, known as a hydro-optical (HO) model, has been generated for all of the Great Lakes that produce robust retrievals annually and intra-annually from satellite ocean color data. The optimized HO model was used to generate long-term time series estimates of several water quality parameters including CHL, SM, CDOM, DOC, attenuation, absorption, backscatter, and photic depth from the MODIS mission (2002-2013). The diffuse attenuation coefficient (Kd) and photic depth are functions of CPA concentration and are therefore inherently retrievable with the CPA-A. Retrieved concentrations of CPA-A derived water quality parameters compare favorably with in situ measurements. The CPA-A algorithm is currently being vetted by NOAA NESDIS (National Environmental Satellite, Data, and Information Service) for operational use in the Great Lakes and distribution to the user community by Great Lakes CoastWatch.