Retrieval of sea surface velocities using sequential Ocean Colour Monitor (OCM) data

The Indian remote sensing satellite, IRS-P4 (Oceansat-I) launched on May 26th, 1999 carried two sensors on board, i.e., the Ocean Colour Monitor (OCM) and the Multi-frequency Scanning Microwave Radiometer (MSMR) dedicated for oceanographic research. Sequential data of IRS-P4 OCM has been analysed over parts of both east and west coast of India and a methodology to retrieve sea surface current velocities has been applied. The method is based on matching suspended sediment dispersion patterns, in sequential two time lapsed images. The pattern matching is performed on a pair of atmospherically corrected and geo-referenced sequential images by Maximum Cross-Correlation (MCC) technique. The MCC technique involves computing matrices of cross-correlation coefficients and identifying correlation peaks. The movement of the pattern can be calculated knowing the displacement of windows required to match patterns in successive images. The technique provides actual flow during a specified period by integrating both tidal and wind influences. The current velocities retrieved were compared with synchronous data collected along the east coast during the GSI cruise ST-133 of R.V. Samudra Kaustubh in January 2000. The current data were measured using the ocean current meter supplied by the Environmental Measurement and CONtrol (EMCON), Kochi available with the Geological Survey of India, Marine Wing. This current meter can measure direction and magnitude with an accuracy of ±5‡ and 2% respectively. The measurement accuracies with coefficient of determination (R2) of 0.99, for both magnitude (cm.s-1) and direction (deg.) were achieved.

[1]  S. Tassan Local algorithms using SeaWiFS data for the retrieval of phytoplankton, pigments, suspended sediment, and yellow substance in coastal waters. , 1994, Applied optics.

[2]  W. Emery,et al.  An objective method for computing advective surface velocities from sequential infrared satellite images , 1986 .

[3]  J. Gao,et al.  Effectiveness of the MCC method in detecting oceanic circulation patterns at a local scale from sequential AVHRR images , 1998 .

[4]  William J. Emery,et al.  Automated extraction of pack ice motion from advanced very high resolution radiometer imagery , 1986 .

[5]  A. C. Vastano,et al.  Sea surface motion over an anticyclonic eddy on the Oyashio Front , 1984 .

[6]  H. Gordon,et al.  Clear water radiances for atmospheric correction of coastal zone color scanner imagery. , 1981, Applied optics.

[7]  W. Emery,et al.  A Comparison of Geometric Correction Methods for Avhrr Imagery , 1984 .

[8]  J. Leese,et al.  An Automated Technique for Obtaining Cloud Motion from Geosynchronous Satellite Data Using Cross Correlation , 1971 .

[9]  Paul E. La Violette,et al.  The Advection of Submesoscale Thermal Features in the Alboran Sea Gyre , 1984 .

[10]  R. Legeckis,et al.  Algorithm for correcting the VHRR imagery for geometric distortions due to the earth curvature, earth rotation and spacecraft roll attitude errors , 1976 .

[11]  J. Cornell Introductory Mathematical Statistics: Principles and Methods , 1970 .

[12]  David Pairman,et al.  Computing advective velocities from satellite images of sea surface temperature , 1992, IEEE Trans. Geosci. Remote. Sens..

[13]  Surface chlorophyll-a distribution in Arabian Sea and Bay of Bengal using IRSP 4 Ocean Colour Monitor satellite data , 2001 .

[14]  I. Robinson,et al.  Sea surface velocities in shallow seas extracted from sequential coastal zone color scanner satellite data , 1989 .

[15]  M. Kamachi Advective surface velocities derived from sequential images for rotational flow field: Limitations and applications of maximum cross-correlation method with rotational registration , 1989 .

[16]  Kathryn A. Kelly,et al.  An Inverse Model for Near-Surface Velocity from Infrared Images , 1989 .