An algorithm for oceanic front detection in chlorophyll and SST satellite imagery

An algorithm is described for oceanic front detection in chlorophyll (Chl) and sea surface temperature (SST) satellite imagery. The algorithm is based on a gradient approach: the main novelty is a shape-preserving, scale-sensitive, contextual median filter applied selectively and iteratively until convergence. This filter has been developed specifically for Chl since these fields have spatial patterns such as chlorophyll enhancement at thermohaline fronts and small- and meso-scale chlorophyll blooms that are not present in SST fields. Linear Chl enhancements and localized (point-wise) blooms are modeled as ridges and peaks respectively, whereas conventional fronts in Chl and SST fields are modeled as steps or ramps. Examples are presented of the algorithm performance using modeled (synthetic) images as well as synoptic Chl and SST imagery. After testing, the algorithm was used on > 6000 synoptic images, 1999–2007, to produce climatologies of Chl and SST fronts off the U.S. Northeast.