Optimization of Sea Surface Current Retrieval Using a Maximum Cross-Correlation Technique on Modeled Sea Surface Temperature
Journal article, 2017

Using sea surface temperature from satellite images to retrieve sea surface currents is not a new idea, but so far its operational near-real-time implementation has not been possible. Validation studies are too region specific or uncertain, sometimes because of the satellite images themselves. Moreover, the sensitivity of the most common retrieval method, the maximum cross correlation, to the parameters that have to be set is unknown. Using model outputs instead of satellite images, biases induced by this method are assessed here, for four different seas of western Europe, and the best of nine settings and eight temporal resolutions are determined. The regions with strong currents return the most accurate results when tracking a 20-km pattern between two images separated by 6-9 h. The regions with weak currents favor a smaller pattern and a shorter time interval, although their main problem is not inaccurate results but missing results: where the velocity is too low to be picked by the retrieval. The results are not impaired by the restrictions imposed by ocean surface current dynamics and available satellite technology, indicating that automated sea surface current retrieval from sea surface temperature images is feasible, for pollution confinement, search and rescue, and even for more energy-efficient and comfortable ship navigation.

Sensitivity studies

Ocean models


In situ oceanic observations

Remote sensing


Céline Heuzé

University of Gothenburg

G. K. Carvajal

Chalmers, Earth and Space Sciences, Radar Remote Sensing

Leif Eriksson

Chalmers, Earth and Space Sciences, Radar Remote Sensing

Journal of Atmospheric and Oceanic Technology

0739-0572 (ISSN) 1520-0426 (eISSN)

Vol. 34 10 2245-2255

Understanding ocean surface dynamics with satelite data

Swedish National Space Board (167/14), 2015-01-01 -- 2019-03-31.

Driving Forces

Sustainable development

Areas of Advance

Building Futures (2010-2018)

Life Science Engineering (2010-2018)

Subject Categories

Earth and Related Environmental Sciences

Oceanography, Hydrology, Water Resources



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