A band-ratio algorithm for retrieving open-lake chlorophyll values from satellite observations of the Great Lakes
journal contributionposted on 2014-02-19, 00:00 authored by Barry M. Lesht, Richard P. Barbiero, Glenn J. Warren
The U.S. Environmental Protection Agency's Great Lakes National Program Office (GLNPO) has collected water quality data from the five Great Lakes annually since 1993. We used the GLNPO observations made since 2002 along with coincident measurements made by the Sea-viewing Wide Field-of-View Sensor (SeaWiFS) and the Moderate-resolution Imaging Spectroradiometer (MODIS) to develop a new band-ratio algorithm for estimating chlorophyll concentrations in the Great Lakes from satellite observations. The new algorithm is based on a third-order polynomial model using the same maximum band ratios employed in the standard NASA algorithms (OC4 for SeaWiFS and OC3M for MODIS). The sensor-specific coefficients for the new algorithm were obtained by fitting the relationship to several hundred matched field and satellite observations. Although there are some seasonal variations in some lakes, the relationship between the observed chlorophyll values and those modeled using the new coefficients is fairly stable from lake to lake and across years. The accuracy of the satellite chlorophyll estimates derived from the new algorithm was improved substantially relative both to the standard NASA retrievals and to previously published algorithms tuned to individual lakes. Monte-Carlo fits to randomly selected subsets of the observations allowed us to estimate the uncertainty associated with the retrievals purely as a function of the satellite data. Our results provide, for the first time, a single simple band ratio method for retrieving chlorophyll concentrations in the offshore "open" waters of the Great Lakes from satellite observations. (c) 2012 International Association for Great Lakes Research.
This work was supported by the USEPA Great Lakes National Program Office as part of EPA Contract No. EP-C-06-085, Scientific and Technical Support with CSC under the direction of Louis Blume, Project Manager.
Publisher StatementNOTICE: This is the author’s version of a work that was accepted for publication in the Journal of Great Lakes Research. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Great Lakes Research, Vol 39, Issue 1, March 2013 DOI: 10.1016/j.jglr.2012.12.007