Validated against Reanalysis Data & Drifters in the Med
Abstract (Format modified for easier reading on the Web)
Near-real time sea surface current information is needed for ocean operations. On a global scale, only satellites can provide such measurements. This can be done with data from infrared radiometers, available on several satellites, thus giving several images a day.
This work analyses the accuracy of such an estimation of surface current fields retrieved with the maximum cross correlation (MCC) method, here used to track patterns of Advanced Very High Resolution Radiometer (AVHRR) brightness temperature between 224 pairs of consecutive images taken between January and December 2015 in the western Mediterranean Sea.
Comparison with in-situ drifters shows that relatively small patterns, moving at a slow speed, tracked between images separated by less than four hours give the best agreement. The agreement was strongest in summer, and consistent with low wind, non-eddying situations.
When compared to a daily reanalysis field, the averaged satellite-retrieved fields showed good agreement, but not the in-situ drifter data.
Drifter data should hence be used to complement satellite-retrieved currents rather than to validate them, since they may measure different components of the surface currents.
Reference Paper Title: “Sea Surface Currents Estimated from Spaceborne Infrared Images Validated against Reanalysis Data and Drifters in the Mediterranean Sea”
Authors: Céline Heuzé 1,* , Gisela K. Carvajal 2, Leif E. B. Eriksson 2 and Monika Soja-Wo?niak 2,3
1 Department of Marine Sciences, University of Gothenburg, 405 30 Gothenburg, Sweden
2 Department of Earth and Space Sciences, Chalmers University of Technology, 412 58 Gothenburg, Sweden
3 CSIRO Commonwealth Scientific and Industrial Research Organisation, Oceans and Atmosphere, 7004 Hobart, Australia
* Author to whom correspondence should be addressed.
Academic Editors: Deepak R. Mishra and Prasad S. Thenkabail
Received: 14 March 2017 / Revised: 19 April 2017 / Accepted: 27 April 2017 / Published: 29 April 2017
Source: http://www.mdpi.com/2072-4292/9/5/422 | Remote Sens. 2017, 9(5), 422; doi:10.3390/rs9050422 (registering DOI)
Note:This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).