Merging the future with the present and past for detecting and evaluating cyanobacterial blooms from

Session: Harmful Algal Blooms and Their Toxicity: Remote Sensing and Modeling Approaches (1)

Richard Stumpf, NOAA, [email protected]
Timothy Wynne, NOAA, [email protected]
Andrew Meredith, NOAA, [email protected]
Sachidananda Mishra, NOAA, [email protected]

Abstract

We have been using satellite imagery to monitor and assess cyanobacterial blooms in Lake Erie for ten years, and in recent years begun examining other Great Lakes, and other U.S. lakes. The key algorithm is the cyanobacterial index (CI), a measure of the chlorophyll found in cyanobacterial blooms.  The CI is a “spectral shape” algorithm, which is quite robust and objective and does not require atmospheric correction, allowing reliable use for many more scenes than analytical algorithms.  Monitoring began with the European Space Agency’s (ESA) MERIS sensor (2002 - 2011).  With the loss of MERIS in the spring of 2012, we shifted to NASA’s MODIS sensor (which can extend bloom observations back to 1999). In 2016, the OLCI was launched by ESA on the Copernicus Sentinel-3 satellite as the replacement for MERIS.  MODIS has more limited bands and resolution than MERIS and OLCI,  but still provides a critical data set for the blooms of 2012-2016, and also provides a bridge from MERIS to OLCI. While ESA continues calibration efforts on OLCI, we are establishing that the algorithm is self-consistent across the three sensors to maintain what will soon be a 20-year record of blooms in the Great Lakes.