Effectiveness of the MCC method in detecting oceanic circulation patterns at a local scale from sequential AVHRR images

The maximum cross-correlation coefficient (MCC) method is a recently devised automatic approach for detecting translational motions from remotely sensed data, and has been commonly used to estimate motion velocities. This paper aims to evaluate the effectiveness of this method in detecting oceanic circulation patterns on a local scale from sequential AVHRR images. It is found that the MCC-derived results from one image pair are indicative of the general flows only. Incoherent flows caused by non-translational motions result in the detected circulation pattern being in loose agreement with the cruising-observed circulation pattern (COCP). Averaging of the directions detected from multiple image pairs slightly improves this agreement. The main factors constraining the performance of the MCC method are identified as rotational and strained motions, isothermal fields, and non-advective processes. Their impact is drastically minimized if the detected results are tested at a significance level of 90 percent or higher. Their elimination from the flow fields causes the retained directions to be more uniform. The larger the number of images used in a detection, the closer the correlation of the detected results with the COCP, especially at a higher significance level. The flow field averaged from three pairs and tested at the 99 percent significance level is most closely correlated with the COCP at a coefficient of 0.728 that is underestimated by 8 percent due to the quality of the COCP.