An Algorithm for Oceanic Front Detection in Chlorophyll and Sea Surface Temperature Satellite Imagery

Igor M. Belkin, Graduate School of Oceanography, University of Rhode Island, ibelkin@gso.uri.edu (Presenting)
John E. O'reilly, Northeast Fisheries Science Center/Narragansett Laboratory, NMFS/NOAA, jay.oreilly@noaa.gov

We developed a new algorithm for oceanic front detection in chlorophyll (Chl) and sea surface temperature (SST) satellite imagery. The algorithm is based on gradient approach. The main novelty is a contextual, shape-preserving, scale-sensitive median filter applied selectively and iteratively until convergence. This filter has been developed specifically for Chl imagery since Chl fields have spatial patterns such as chlorophyll enhancement along thermohaline fronts and small- and meso-scale chlorophyll blooms and patches that are not present in SST fields. Chl patterns are modeled as ridges and peaks; they need to be preserved and treated differently from SST fronts modeled as steps or ramps. Satellite data from several thermal and color sensors (AVHRR, SeaWiFS and MODIS/Terra and Aqua) were processed with the new algorithm to generate climatology of SST and Chl fronts off the U.S. Northeast, encompassing the Mid-Atlantic Bight, Georges Bank and Gulf of Maine. This area has a wide variety of fronts such as the Gulf Stream; Shelf-Slope Front; tidal mixing fronts of Georges Bank and Nantucket Shoals; and water mass fronts of the Gulf of Maine. Most fronts are steered by bathymetry. We constructed a 10-year frontal climatology for SST and Chl for the period 1997-2007. Examples are presented of the algorithm performance over a broad range of spatial and temporal scales, using modeled (synthetic) images as well as Chl and SST imagery and their frontal climatology.