An algorithm to improve the detection of ocean fronts from whiskbroom scanner images

High-resolution satellite imagery is a valuable data source to analyse ocean submesoscale dynamics (i.e., with spatial scales of the order of 1–10 km) and investigate their impact on turbulent mixing, energetics of mesoscale vortices, instability processes or phytoplankton blooms. However, data acquired by satellite sensors often suffer from instrumental noise that degrades image quality and therefore compromises the detection of ocean fronts as well as the estimation of its physical characteristics. A well-known artefact in data characteristic of whiskbroom scanners is stripe noise. In this article, we propose an algorithm that improves the detection of ocean fronts by removing the impact of striping on the observed gradient field. We use level 2 sea surface temperature and chlorophyll-a products derived from NASA’s Moderate Resolution Imaging Spectroradiometer to illustrate the algorithm performance.