Computing Coastal Ocean Surface Currents From Infrared and Ocean Color Satellite Imagery

Many previous studies have demonstrated the viability of estimating advective ocean surface currents from sequential infrared satellite imagery using the maximum cross-correlation (MCC) technique when applied to 1.1-km-resolution Advanced Very High Resolution Radiometer (AVHRR) thermal infrared imagery. Applied only to infrared imagery, cloud cover and undesirable viewing conditions (gaps in satellite data and edge-of-scan distortions) limit the spatial and temporal coverage of the resulting velocity fields. In addition, MCC currents are limited to those represented by the displacements of thermal surface patterns, and hence, isothermal flow is not detected by the MCC method. The possibility of supplementing MCC currents derived from thermal AVHRR imagery was examined, with currents calculated from 1.1-km-resolution Moderate Resolution Imaging Spectroradiometer (MODIS) and Sea-viewing Wide Field-of-view Sensor (SeaWiFS) ocean color imagery, which often have spatial patterns complementary to the thermal infrared patterns. Statistical comparisons are carried out between yearlong collections of thermal and ocean color derived MCC velocities for the central California Current. It is found that the image surface patterns and resulting MCC velocities complement one another to reduce the effects of poor viewing conditions and isothermal flow. The two velocity products are found to agree quite well with a mean correlation of 0.74, a mean rms difference of 7.4 cm/s, and a mean bias less than 2 cm/s which is considerably smaller than the established absolute error of the MCC method. Merging the thermal and ocean color MCC velocity fields increases the spatial coverage by approximately 25% for this specific case study

I. Introduction

A Major problem in physical oceanography is the challenge of mapping the complex mesoscale structure of surface currents in the coastal regions of the world's oceans. Any surface-current mapping method must be capable of resolving these mesoscale features and their variations in time and space in order to study the characteristics of these currents. Earlier studies have demonstrated that the maximum cross-correlation (MCC) feature tracking method can be applied to sequential 1.1-km Advanced Very High Resolution Radiometer (AVHRR) thermal infrared imagery to estimate the mesoscale surface-current field. This technique has been proven to be useful in mapping the short space and time scale structures of the East Australian Current [1], [2], the Gulf Stream [3], the California Current (CC) [4], [5], and the coastal waters off British Columbia [6]. However, the MCC method is often limited by thermal imagery with low surface gradients, undesirable viewing conditions (cloud cover, gaps in satellite data and coverage, and edge-of-scan distortions), and isothermal flow. These image characteristics result in MCC velocity fields that have highly variable spatial and temporal coverage.