Document Type

Article

Publication Date

9-8-2017

Publication Title

IEEE Transactions on Geoscience and Remote Sensing

ISSN

0196-2892

Volume

55

Issue

12

DOI

http://dx.doi.org/10.1109/TGRS.2017.2741924

Abstract

One of the significant challenges in physical oceanography is getting an adequate space/time description of the ocean surface currents. One possible solution is the maximum cross-correlation (MCC) which we apply to hourly ocean color (OC) images from the Geostationary Ocean Color Imager (GOCI) over a 5-year long time period. Since GOCI provided a large number of MCC image pairs to process we introduce a new MCC search strategy to improve the computational efficiency of the MCC method saving 95.9% of the processing time. We also used a MCC current overlap method to increase the total spatial coverage of the currents, proving a 25.6% increase. A 5-year mean and seasonal time-average flows were computed for capturing the major currents in the area of interest (AOI). The mean flows investigate that the Kuroshio path, support the triple-branch pattern of the Tsushima Warm Current (TC) and reveal the origin of the TC. The evolution of the Kuroshio warm-core ring near the east coast of Japan is revealed by three monthly MCC composites. We capture the evolution of the Kuroshio meander over seasonal, monthly and weekly time scales. Three successive weekly MCC composite maps demonstrate how a large anticyclonic eddy, to the south of the Kuroshio meander, influences its formation and evolution in time and space. The unique ability to view short space/time scale changes in these strong current systems is a major benefit of the application of the MCC method to the high spatial resolution and rapid refresh GOCI data.

Comments


This is a post-print version of "Computing Ocean Surface Currents From GOCI Ocean Color Satellite Imagery" published in IEEE Transactions on Geoscience and Remote Sensing.

J. Liu, W. J. Emery, X. Wu, M. Li, C. Li and L. Zhang, "Computing Ocean Surface Currents From GOCI Ocean Color Satellite Imagery," in IEEE Transactions on Geoscience and Remote Sensing, vol. 55, no. 12, pp. 7113-7125, Dec. 2017.
doi: 10.1109/TGRS.2017.2741924

©Copyright 2017 IEEE

Available for download on Sunday, September 08, 2019

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