Extracting Multiyear Surface Currents from Sequential Thermal Imagery Using the Maximum Cross-Correlation Technique

Melissa M. Bowen Colorado Center for Astrodynamics Research, University of Colorado, Boulder, Colorado, and National Institute of Water and Atmospheric Research, Auckland, New Zealand

Search for other papers by Melissa M. Bowen in
Current site
Google Scholar
PubMed
Close
,
William J. Emery Colorado Center for Astrodynamics Research, University of Colorado, Boulder, Colorado

Search for other papers by William J. Emery in
Current site
Google Scholar
PubMed
Close
,
John L. Wilkin National Institute of Water and Atmospheric Research, Auckland, New Zealand

Search for other papers by John L. Wilkin in
Current site
Google Scholar
PubMed
Close
,
Paul C. Tildesley CSIRO Marine Research, Hobart, Tasmania, Australia

Search for other papers by Paul C. Tildesley in
Current site
Google Scholar
PubMed
Close
,
Ian J. Barton CSIRO Marine Research, Hobart, Tasmania, Australia

Search for other papers by Ian J. Barton in
Current site
Google Scholar
PubMed
Close
, and
Rebecca Knewtson Colorado Center for Astrodynamics Research, University of Colorado, and Ball Aerospace and Technologies Corp., Boulder, Colorado

Search for other papers by Rebecca Knewtson in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

Ocean surface circulation can be estimated by automated tracking of thermal infrared features in pairs of sequential satellite imagery. A 7-yr time series of velocity, extracted from thermal imagery of the East Australian Current using the maximum cross-correlation (MCC) technique, provides enough measurements for a more statistical evaluation of the method than has previously been possible. Excluding 1 yr with extensive cloud cover, the method produces about 8000 velocity estimates per month with some seasonal variation. Method precision is estimated to be between 0.08 and 0.2 m s–1 rms, the lower value with more restrictive compositing. Mean flow, time-dependent flow, and eddy kinetic energy from the time series are compared with values derived from a dynamic height climatology, altimeter analyses, and drifter datasets in the region. The observations reproduce similar features in the flow. The differences between the observations are discussed in relation to noise in the methods and differences in the types of velocities they measure.

Current affiliation: Institute of Marine and Coastal Sciences, Rutgers University, New Brunswick, New Jersey

Corresponding author address: Dr. Melissa M. Bowen, NIWA, P.O. Box 14-901, Kilbirnie, Wellington, New Zealand. Email: m.bowen@niwa.cri.nz.

Abstract

Ocean surface circulation can be estimated by automated tracking of thermal infrared features in pairs of sequential satellite imagery. A 7-yr time series of velocity, extracted from thermal imagery of the East Australian Current using the maximum cross-correlation (MCC) technique, provides enough measurements for a more statistical evaluation of the method than has previously been possible. Excluding 1 yr with extensive cloud cover, the method produces about 8000 velocity estimates per month with some seasonal variation. Method precision is estimated to be between 0.08 and 0.2 m s–1 rms, the lower value with more restrictive compositing. Mean flow, time-dependent flow, and eddy kinetic energy from the time series are compared with values derived from a dynamic height climatology, altimeter analyses, and drifter datasets in the region. The observations reproduce similar features in the flow. The differences between the observations are discussed in relation to noise in the methods and differences in the types of velocities they measure.

Current affiliation: Institute of Marine and Coastal Sciences, Rutgers University, New Brunswick, New Jersey

Corresponding author address: Dr. Melissa M. Bowen, NIWA, P.O. Box 14-901, Kilbirnie, Wellington, New Zealand. Email: m.bowen@niwa.cri.nz.

Save
  • Barton, I. J., 2002: Ocean currents from successive satellite images: The reciprocal filtering technique. J. Atmos. Oceanic Technol., 19 , 16771689.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cote, S., and Tatnall A. R. L. , 1995: The Hopfield neural network as a tool for feature tracking and recognition from satellite sensor images. Int. J. Remote Sens., 18 , 871885.

    • Search Google Scholar
    • Export Citation
  • Domingues, C. M., Goncalves G. A. , Ghisolfi R. D. , and Garcia C. A. E. , 2000: Advective surface velocities derived from sequential infrared images in the southwestern Atlantic Ocean. J. Remote Sens., 73 , 218226.

    • Search Google Scholar
    • Export Citation
  • Emery, W. J., and Thomson R. E. , 1998: Data Analysis Methods in Physical Oceanography. Pergamon, 634 pp.

  • Emery, W. J., Thomas A. C. , Collins M. J. , Crawford W. R. , and Mackas D. L. , 1986: An objective method for computing advective surface velocities from sequential infrared satellite images. J. Geophys. Res., 91 , 1286512878.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Emery, W. J., Fowler C. , and Clayson C. A. , 1992: Satellite-image-derived Gulf Stream currents compared with numerical model results. J. Atmos. Oceanic Technol., 9 , 286304.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Godfrey, J. S., Cresswell G. R. , Golding T. J. , and Pearce A. F. , 1980: The separation of the East Australian Current. J. Phys. Oceanogr., 10 , 430440.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Holyer, R. J., and Peckinpaugh S. H. , 1989: Edge detection applied to satellite imagery of the oceans. IEEE Trans. Geosci. Remote Sens., 27 , 4656.

  • Kelly, K. A., 1989: An inverse model for near-surface velocities from infrared images. J. Phys. Oceanogr., 19 , 18451864.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kelly, K. A., and Strub P. T. , 1992: Comparison of velocity estimates from Advanced Very High Resolution Radiometer in the coastal transition zone. J. Geophys. Res., 97 , 96539668.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kriebel, K. T., Saunders R. W. , and Gesell G. , 1991: Optical properties of clouds derived from fully cloudy AVHRR pixels. Contrib. Atmos. Phys., 42 , 1471.

    • Search Google Scholar
    • Export Citation
  • LeTraon, P. Y., and Hernandez F. , 1992: Mapping the oceanic mesoscale circulation: Validation of satellite altimetry using surface drifters. J. Atmos. Oceanic Technol., 9 , 687698.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, A. K., Marine S. , and Kwok R. , 1997: Tracking of ice edges and ice flows by wavelet analysis of SAR images. J. Atmos. Oceanic Technol., 14 , 11871198.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mata, M. M., Tomczak M. , Wijffels S. , and Church J. A. , 2000: East Australian Current volume transport at 30°S: Estimates from the World Ocean Circulation Experiment hydrographic sections PR11/P6 and the PCM3 current meter array. J. Geophys. Res., 105 , 2850928526.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nilsson, C. S., and Cresswell G. R. , 1981: The formation and evolution of East Australian Current warm-core eddies. Progress in Oceanography, Vol. 9, Pergamon Press, 133–183.

    • Search Google Scholar
    • Export Citation
  • Ohlmann, J. C., Niiler P. P. , Fox C. A. , and Leben R. R. , 2001: Eddy energy and shelf interactions in the Gulf of Mexico. J. Geophys. Res., 106 , 26052620.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ridgway, K. R., Dunn J. R. , and Wilkin J. L. , 2002: Ocean interpolation by four-dimensional weighted least squares—Application to the waters around Australasia. J. Atmos. Oceanic Technol., 19 , 13571375.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schmetz, J., and Nuret M. , 1987: Automatic tracking of high-level clouds in Meteosat infrared images with a radiance windowing technique. Eur. Space Agency J., 11 , 275286.

    • Search Google Scholar
    • Export Citation
  • Strub, P. T., Chereskin T. K. , Niiler P. P. , James C. , and Levine M. D. , 1997: Altimeter-derived variability of surface velocities in the California Current system. J. Geophys. Res., 102 , 1272712748.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tokmakian, R., Strub P. T. , and McClean-Padman J. , 1990: Evaluation of the maximum cross-correlation method of estimating sea surface velocities from sequential satellite images. J. Atmos. Oceanic. Technol., 7 , 852865.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vigan, X., Provost C. , Bleck R. , and 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 , 1949919514.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vigan, X., and Podesta P. , 2000b: Sea surface velocities from sea surface temperature image sequences 2. Application to the Brazil–Malvinas Confluence area. J. Geophys. Res., 105 , 1951519534.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wilkin, J. L., Bowen M. M. , and Emery W. J. , 2002: Mapping mesoscale currents by optimal interpolation of satellite radiometer and altimeter data. Ocean Dynamics, 52 , 95103.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zavialov, P. O., Ghisolfi R. D. , and Garcia C. A. E. , 1998: An inverse model for seasonal circulation over the southern Brazilian shelf: Near-surface velocity from the heat budget. J. Phys. Oceanogr., 28 , 545562.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 534 155 21
PDF Downloads 291 71 10