Current Tracking in the Mediterranean Sea Using Thermal Satellite Imagery

  • S. Dransfeld

Infrared images representing the thermal state of the ocean surface may be used to track ocean currents with feature-tracking algorithms that identify and follow temperature gradients and hence represent advective surface motion. One of these techniques, the Maximum Cross Correlation technique, is based on a comparison of individual subscenes of sequential images, to estimate where a feature has moved from one image to the next. It has distinct advantages compared to alternative feature tracking algorithms such as its simplicity and robustness. Previous research has shown it to achieve a precision of 0.08 to 0.20 m/s rms. This study focuses on surface currents in the central Mediterranean Sea by analysing sequential Advanced Very High Resolution Radiometer local area coverage 1.1 km resolution images from June 2003. Most attention is placed onto the presentation of the method, the results and the main ad/disadvantages of using a Maximum Cross Correlation approach.

Keywords

Vortex Covariance Radar Coherence Advection 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Bowen MM, Emery WJ, Wilkin JL, Tildesley PC, Barton IJ, Knewtson R (2002) Extracting Multiyear Surface Currents from Sequential Thermal Imagery Using the Maximum Cross-Correlation Technique. Journal of Atmospheric and Oceanic Technology 19: 1665-1676CrossRefGoogle Scholar
  2. Béthoux JP (1980) Mean water fluxes across sections in the Mediterranean Sea, evaluated on the basis of water and salt budgets and of observed salinities. Oceanol Acta 3: 79-88Google Scholar
  3. Cote S, Tatnall ARL (1995) The Hopfield neural network as a tool for feature tracking and recognition from satellite sensor images. Inte J Rem Sens 18: 871-885Google Scholar
  4. Domingues CM, Goncalves GA, Ghisolfi RD, Garcia CAE (2000) Advective Surface Velocities Derived from Sequential Infrared Images in the Southwestern Atlantic Ocean. Rem Sens Environ 73 (2): 218-226CrossRefGoogle Scholar
  5. Dransfeld S, Larnicol G, LeTraon PY (2006) The potential of maximum cross-correlation technique to estimate surface currents from thermal AVHRR global coverage data. IEEE Geoscience and Rem Sens Letters 3 (4): 508-511CrossRefGoogle Scholar
  6. Emery WJ, Thomas AC, Collins MJ, Crawford WR, Mackas DL (1986) An Objective Method for Computing Advective Surface Velocities From Sequential Infrared Satellite Images. J Geophys Res 91 (C11): 12,865-12,878Google Scholar
  7. Emery WJ, Fowler C, Clayson CA (1992) Satellite-image-derived Gulf Stream currents compared with numerical model results. Journal of Atmospheric and Oceanic Technology 19: 286-304CrossRefGoogle Scholar
  8. Emery WJ, Crocker I, Matthews D, Baldwin D (2006) Computing Ocean Surface Currents from Infrared and Ocean Color Imagery. Trans Geosci Rem Sens (in press)Google Scholar
  9. Garcia, AEC, Robinson IS (1989) Sea Surface Velocities in Shallow Seas Extracted from Sequential Coastal Zone Color Scanner Satellite Data. J Geophys Res 94 (C9): 12,681-12,691CrossRefGoogle Scholar
  10. Gasparini GP, Smeed DA, Alderson S, Sparnocchia S, Vetrano A, Mazzola S (2004) Tidal and subtidal currents in the Strait of Sicily. J Geophys Res 109 (C2), C02011, doi: 10.1029/2003JC002011CrossRefGoogle Scholar
  11. Holyer RJ, Peckinpaugh SH (1989) Edge detection applied to satellite imagery of the oceans. IEEE Trans Geosci Rem Sens 27: 46-56CrossRefGoogle Scholar
  12. Kelly KA (1989) An inverse model for near-surface velocity from infrared images. J Phys Oceanogr 19: 1845-1864CrossRefGoogle Scholar
  13. Kelly KA, Strub PT (1992) Comparison of velocity estimates from Advanced Very High Resolution Radiometer in the coastal transition zone. J Geophys Res 97 (C6): 9653-9668CrossRefGoogle Scholar
  14. Liu AK, Martin S, Kwok R (1997) Tracking of Ice Edges and Ice Floes by Wavelet Analysis of SAR Images. Journal of Atmospheric and Oceanic Technology 14: 1187-1198CrossRefGoogle Scholar
  15. Millot C, Taupier-Letage I (2005) Circulation in the Mediterranean Sea. In: Saliot A (ed) The Mediterranean Sea, Handbook of Environmental Chemistry, vol 5, Water Pollution, Part K. Springer-Verlag, Berlin Heidelberg, pp 29-66CrossRefGoogle Scholar
  16. Ollta A, Sorgente R, Rlbottl A, Natale S, Gabersek S (2006) Effects of the 2003 European heatwave on the Central Mediterranean Sea surface layer: a numerical simulation. Ocean Science Discussions 3: 85-125CrossRefGoogle Scholar
  17. Onken R, Robinson AR, Lermusiaux PFJ, Haley Jr. PJ, Anderson LA (2003) Data-driven simulations of synoptic circulation and transports in the Tunisia-Sardinia-Sicily region. J Geophis Res 108 (C9): 8123-8136CrossRefGoogle Scholar
  18. Robinson AR, Golnaraghi M, Leslie WG, Artegiani A, Hecht A, Lazzoni E, Michelato A, Sansone E, Teocharis A, Unluata U (1991) The Eastern Mediterranean general circulation: features, structure and variability. Dynamics of Atmospheres and Oceans 15: 215-240CrossRefGoogle Scholar
  19. Tokmakian RT, Strub PT, McClean-Padman J (1990) Evaluation of the maximum cross-correlation method of estimating sea surface velocities from sequential satellite images. J Atmos Ocean Tech 7: 852-865CrossRefGoogle Scholar
  20. Vigan X, Provost C, Bleck R, Courtier P (2000a) Sea surface velocities from sea surface temperature image sequences. 1, Method and validation using primitive equation model output. J Geophys Res 105: 19499-19514CrossRefGoogle Scholar
  21. Wilkin JL, Bowen M, Emery WJ (2002) Mapping mesoscale currents by optimal interpolation of satellite radiometer and altimeter data. Ocean Dynamics 52: 95-103CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V 2008

Authors and Affiliations

  • S. Dransfeld
    • 1
  1. 1.Institute of OceanographyUniversität of HamburgGermany

Personalised recommendations