Maximum Cross Correlation Method
The ocean surface
velocity products provided on this site are generated by applying the
Maximum Cross Correlation (MCC) method to sequential thermal satellite imagery.
The MCC method ,  is an automated procedure that calculates the
displacement of small regions of patterns from one image to another.
The procedure, illustrated in Fig. 1, cross-correlates a template
subwindow in an initial image with all possible subwindows of the same
size that fall within the search window of a second image. The location
of the subwindow in the second image that produces the highest
cross-correlation with the subwindow in the first image indicates the
most likely displacement of that feature. The velocity vector is then
calculated by dividing the displacement vector by the time separation
between the two images.
1. The MCC method. The solid boxes in the first image are referred to
"template subwindows"; this is the pattern to search for in the second
image. The dashed boxes in the second image are referred to as "search
The size of the template
subwindow is a balance between containing enough features for tracking
(and hence having enough degrees of freedom for a statistically
significant correlation) and smoothing out the structure of the
flow. A 22x22 pixel template subwindow is used for all realtime
MCC processing. The search window in the second image must be large
enough to accommodate the largest expected velocity. A maximum velocity
of 70 cm/s is used for West Coast MCC processing, and 80 cm/s is
used for East Coast processing.
A raw MCC output velocity field
contains vectors at every grid point, many of which result from low
correlations. To capture velocities that accurately depict the ocean
surface currents, the raw MCC vectors are put through a strict
filtering process. Velocities that meet the following filtering
requirements are retained and considered accurate:
- a cross-correlation coefficient > 0.8
- the displacement of the subwindow in the second
image (relative to that in the first image) must be greater than 1 pixel
- the velocity must have 4 neighboring (within 2
grid points) velocities that have x- and y- components within 10 cm/s
- the velocity must have 4 neighboring (within 2
grid points) velocities that have a direction within 50 degrees
After the filtering process the
accurate velocities are composited over a specified period of time.
We provide both 3-day and 7-day composites. The composite
velocities are also filtered using the following requirements as a
- 6 (west
coast) or 8 (east coast) neighboring (within 3 grid points)
velocities that have x- and y- components within 10 cm/s
- 6 (west coast) or 8 (east coast)
neighboring (within 3 grid points) velocities that have a direction
within 50 degrees
isothermal/isochromatic ocean surface conditions drastically limit the
spatial and temporal velocity coverage provided by the MCC method.
Clouds block the ocean surface in both thermal and ocean color imagery,
and there are no features for the MCC method to track in
isothermal/isochromatic regions. As a result, it is common for there to
be a limited number of MCC velocities in the composites.
For our realtime processing
system, the MCC method is applied to thermal IR (11 micron) imagery
acquired by the Advanced Very High Resolution Radiometers (AVHRR) on
board the NOAA-class satellites, and thermal IR imagery acquired by
the MODerate resolution Imaging Spectroradiometers (MODIS) on board the
AQUA and TERRA satellites. The thermal and ocean color data
products have a pixel size of ~1 km. The AVHRR images are geolocated
using the method described in . Crocker et al. 2007  have
shown that MCC velocities derived from ocean color imagery are similar
to those derived from thermal imagery, and these two MCC products can
be merged to increase the overall spatial and temporal velocity
coverage. The ocean color imagery is not used for realtime current generation due to lack of sufficient data after failure of one of the MODIS ocean color sensors.
Fig. 2. Four sequential thermal
images from the U.S. east coast, and the resulting MCC composite
 W. J.
Emery, A. C. Thomas, M. J. Collins, W. R. Crawford, and D. L. Mackas,
1986: An objective method for computing advective surface velocities
from sequential infrared satellite images. J. Geophysical Res., 91, 12
 R. M. Ninnis, W. J. Emery and M. J. Collins, 1986:
Automated extraction of pack ice motion from advanced very high
resolution radiometry. J. Geophysical Res., 91, 10, 725-734.
 W. J. Emery, D. Baldwin, and D. K. Matthews,
2003: Maximum Cross Correlation Automatic Satellite Image
Navigation and Attitude Corrections for Open Ocean Image
Navigation. Geoscience and Remote Sensing 41, 33-42.
 R. I. Crocker, D. K. Matthews, W. J. Emery, and D. Baldwin,
2007: Computing Coastal Ocean Surface Currents From Infrared and Ocean Color Satellite Imagery. Geoscience and Remote Sensing 45, 435-447.