Automated ocean front feature mapping using Sentinel-1 with examples from the Gulf Stream

Abstract

ABSTRACT This study assessed the ability of Sentinel-1 radial velocity (RVL) products to mark the position of ocean current front features, using the Gulf Stream (GS) as a case study. RVL-derived front features were compared to fronts derived from Multi-scale Ultra-high Resolution Sea Surface Temperature Analysis (MURSST) data. A ridge filter was used to find fronts in both the Sea Surface Temperature (SST) and RVL data, and the similarity between each pair of fronts was measured using the discrete Hausdorff Distance (HD) and Mean Hausdorff Distance (MHD). Front features were correctly identified in concurrent SST and RVL data pairs in 65% of cases. The automatic front extraction sometimes failed by misclassifying an eddy or similar ocean feature as the ocean current in either the RVL or SST image, and sometimes failed to extract the entire length of the front visible within the image. SST and RVL fronts were classified manually to determine the success rate of the automatic front extraction, and to exclude front extraction errors from further analysis. The results demonstrated that RVL products were effective at determining the location of ocean fronts where the angle between the front’s normal vector and the sensor’s azimuthal heading is less than ~ 40°. A mean HD of 25.5 km and a mean MHD of 10.8 km was calculated for all front pairs in the study area.