Operational ice type classification and water quality satellite retrievals for the Great Lakes
Session: Remote Sensing, Visualization, and Spatial Data Applications for the Great Lakes (3)
George Leshkevich, Great Lakes Env. Research Lab, NOAA, [email protected]
Son Nghiem, Jet Propulsion Laboratory/California Institute of Technology, [email protected]
Robert Shuchman, Michigan Technological University, [email protected]
Mike Sayers, Michigan Tech. Research Inst., [email protected]
Karl Bosse, Michigan Tech Research Inst., [email protected]
Reid Sawtell, Michigan Tech Research Inst., [email protected]
Abstract
Using our measured library of calibrated polarimetric C-band SAR ice backscatter signatures, an algorithm was developed to classify and map major Great Lakes ice types using satellite SAR data. ICECON, a risk assessment tool developed for the Coast Guard, uses 6 categories of ice severity for icebreaking operations. To support the ICECON ice severity index, the algorithm was modified to output ice types to adhere to the 6 ICECON categories. Ranges of ice thickness were assigned to each category. Moreover, measured light transmittance through each major ice type can be used to estimate lake-wide under ice transmittance.
Using our all season, multi-year measured set of inherent optical properties with concurrent CPA concentrations for all the Great Lakes, an algorithm (Color Producing Agent Algorithm (CPA-A)), a semi-analytical inverse radiative transfer bio-optical model, to retrieve water quality parameters, namely chlorophyll (CHL), suspended mineral (SM), and CDOM, from satellite observed reflectance was developed that produces robust retrievals from any ocean color satellite data that compare favorably with in situ measurements.
These algorithms to retrieve Great Lakes ice types/thickness and water quality parameters from satellite data have been transferred to NOAA/NESDIS for operational implementation. Products are available via the Great Lakes CoastWatch website (https://coastwatch.glerl.noaa.gov).