SeaWinds Resolution Enhancement: Initial Results
David G. Long
Oct. 21, 1997
This is an experiemental web page to report the initial results
of the resolution enhancement of Seawinds scatterometer measurements.
Basic idea: given one rev of simulated measurements (Level 1.5),
multiple revs of noisy sigma-0 measurements of a small test region
were simulated. Both full `egg' and slice measurements were simulated.
Non-enhanced and SIRF-enhanced images were then generated from the
simulated measurements. The results clearly show that the slices
result in higher resolution images than the eggs, even if resolution
enhancement is used. In addition, though the individual Seawinds
sigma-0 measurements are noisier than NSCAT measurements, they better
cover the ocean's surface, requiring less data to make images. Adequate
quality images can be produced with only 1 day of data (versus 3
for NSCAT). This will be useful in tracking rapid temporal variations
in sea ice.
NSCAT sigma-0 measurements have proved to be very useful in land
and ice studies. So much so, that we desire to continue making global
Ku-band measurements of sigma-0 from scatterometer measurements
in the future. Unfortunately, the original design for Seawinds produced
measurements of much lower resolution than NSCAT, which would limit
the utility of the Seawinds measurements compared to NSCAT measurements
when used in land/ice science studies. The measurement resolution
was determined by the area of the pencil-beam footprint on the earth
(known as the `egg'). This discrepancy between NSCAT and Seawinds
resolution motivated the motification of the Seawinds design to
enable measurement of sigma-0 over small regions (known as `slices')
of the pencil-beam footprint. The improved resolution of the measurements
is useful for both wind retrieval and land and ice studies. The
improved wind measurement resolution is particularly useful near
The purpose of this report is to briefly consider the effects
of the change from eggs to slices on land/ice imaging. The effects
of applying the SIRF resolution enhancement algorithm to both cases
is studied. The results show that the slices result in significantly
better resolution than the egg measurements and that although the
measurement noise in the slice measurements is higher than for the
eggs, the SIRF algorithm works well with the slices to produce high
resolution sigma-0 images. In the following sections I describe
the simulation technique used, present sample results, and finally
As a pencil beam scatterometer, Seawinds has a much different measurement
geometry than NSCAT. The scanning geometry produces a denser sampling
of surface for Seawinds with higher signal-to-noise ratios than
NSCAT. However, the dwell time is much smaller for Seawinds. As
a result the individual sigma-0 measurements tend to be noiser for
Seawinds than for NSCAT.
NSCAT makes measurements of sigma-0 over a range of incidence
angles. The dependence of sigma-0 on incidence angle, theta, is
Sigma-0 (dB) = A + B (40-theta)
where `A' is the incidence angle normalized sigma-0 in dB (sigma-0
in dB at 40 deg) and `B' is the dependence of sigma-0 on incidence
angle in dB/deg. The Scatterometer Image Reconstruction with Filtering
(SIRF) algorithm makes enhanced resolution images of A and B from
the sigma-0 measurements. Although `B' images can not be made from
Seawinds measurements, for convenience, all the of the images shown
are expressed as `A' images.
One of the big (pun intended) differences between NSCAT and Seawinds
is the size of the data files. While one rev of NSCAT L1.5 data
occupies 20 MB, the equivalent sample Seawinds file (L1B_kpm0.7.DAT)
kindly supplied by Vincent Hsiao is 1.2 GB in size! (The sample
file has not been optimized in any way so should end up more like
200 MB when in final form). Unlike wind retrieval, land/ice imaging
requires multiple passes (revs) to generate useful images. The large
file sizes can make multiple rev simulations difficult.
We have selected a very small region to evaluate the imaging resolution
because of the large file sizes involved. Even so, the intermediate
`setup' file for the very small test region is over 150MB for Seawinds
compared to 4.2 MB for NSCAT. For reference, an Antarctic NSCAT
image requires a setup file of >350MB. Making Seawinds images will
require A LOT more CPU/disk resources than does NSCAT!
For convenience, I am using a small area in Antarctica to extract
the sampling geometry and cells to create the simulated images.
The region is approximately 1000 km x 800 km and is centered at
-74.5 deg latitude and 128.5 deg longitude. This area is observed
approximately 5 times per day by Seawinds, though some passes only
cover part of the region. (Note that a similar size region near
the equator would be observed somewhat less often.)
Instead of having Vincent generate multiple rev files and since
this is a simulation, I've taken the single rev of data and generated
synthetic revs by merely adjusting the longitudes of the measurements
at a rate of 360*101/1440 deg/rev. Assuming that the rotation rate
is not synchronized to the ascending node, I also added a small
amount of latitude `jitter' to the measurement locations.
Vicent's file contains the corner location of the slices and their
center. To compute the eggs, I used the outline of all the slices.
This is not completely accurate but is close. Figure 1 shows the
outline and relative locations of 3 succeeding eggs from one record.
The upper left eggs are the outer scan while the lower right are
the inner scan. Figure 2 shows the outlines of the center 10 slices
of the first two inner beam measurements. Figure 3 shows the egg
locations for multiple scans over a small region.
Fig. 1: Egg locations from the simulation file consisting of six
consecutive measurements in the file. The outline of the 12 slices
in the file are shown.
Fig. 2: Slice locations from the simulation file for the first
two inner scan measurements. Only the center 12 slices are shown.
Fig. 3: Egg locations for part of one pass of a small test region.
Simulated sigma-0 measurements are generated by laying the sigma-0
slices onto a synthetic image of the surface (see Fig. 4 below).
The effective sigma-0 is the weighted average of the pixels of the
synthetic image. Then, given sigma-0, the coefficients of Kpc and
the information in the file, Monte Carlo noise is added to generate
a simulated sigma-0 measurement. The simulated measurements are
used in the imaging below.
Fig. 4: Synthetic `truth' A image. Grayscale range is from -20
to -5. Note that `A' is sigma-0 normalized to 40 deg incidence angle.
This synthetic truth image is one we've used extensively. It simulates
an Amazon-basin like image.
Just for reference, Fig. 5 shows the results of applying the SIRF
resolution enhancement imaging algorithm to simulated NSCAT measurements
of this same region and synthetic image. Note, however, that 6 days
of v-pol NSCAT data have been used. NSCAT can achieve higher resolution
Note that while the original synthetc image is rectangular, the
projected study region does not fill the full output image area
due to the equal area Lambert map projection. Comparing the NSCAT
and truth image shows that NSCAT does a pretty good job of recovering
the details of the truth image with some smearing along the edges.
Fig. 5: SIRF'd enhanced resolution (~4.5 km pixel resolution)
A image generated from 6 days of simulated NSCAT measurements. Grayscale
range is from -20 to -5.
A number of cases will be compared. Using the simulated sigma-0
measurements, images were computed using (1) the traditional gridding
approach and (2) the SIRF resolution enhancement technique for both
eggs and slices. For the examples presented below, all the non-enhanced
grid images have a resolution of approximately 25 km while the SIRF
image have a pixel resolution of approximately 4.5 km.
Because the Seawinds measurements densely overlap (and we get
lots of measurements per pass, particularly for the slices), reasonable
images can be made from only one day of data in this polar region.
However, as will be illustrated, the noise level in the images can
be reduced if multiple days are combined. NSCAT can make reasonable
v-pol images in 3 days, though for the polar region six days are
used since this is the period required to make good quality h-pol
Gridded (non-enhanced) images
Figure 6 shows a gridded image created from Seawinds eggs. Note
that while hints of key features are visible these are poorly represented
and may not be fully visible. For comparison, Fig. 7 shows a gridded
image produced from the slice measurements. We note the improved
resolution of the image figures. (Fig. 8 is a repeat of the truth
image inserted to make the comparison to the truth image easier.)
Fig. 6: Gridded (25 km resolution) A image generated from 1 day
of simulated `egg' measurements. Grayscale range is from -20 to
Fig.7: Gridded (25 km resolution) A image generated from two days
of simulated `slice' measurements. Grayscale range is from -20 to
Fig. 8: Synthetic `truth' A image. Grayscale range is from -20
Resolution enhanced images
For comparison with previous images, I have produced images using
a modified form of the NSCAT SIRF algorithm. I note that I have
not `tuned' the SIRF algorithm for the Seawinds case. In particular,
I have not incorporated antenna pattern weighting into the algorithm
but have used a uniform weighting. As a result, it should be possible
to generate better quality images than the ones presented here.
Figure 9 shows a SIRF'ed image generated from egg measurements
while Fig. 10 shows a SIRF'ed image generated from slice measurements.
Note that SIRF'ing the data improves the effective resolution and
visibility of key features. The enhancement is most profound for
the egg measurements, though the enhanced slice images have better
resolution. While noisy, the SIRF'ed slice measurement is excellent.
I extended the imaging period to use two days of data to produce
Fig. 11 which is a SIRF'ed slice image. This image has similar resolution
to the one day image but lower noise due to the additional measurements
in the longer time period. Note that longer periods further reduce
the noise. Also note that the two day SIRF'ed slice image is nearly
as good as the NSCAT image, suggesting that Seawinds can be used
for many of the same studies of NSCAT. Longer time periods can improve
the quality the SIRF'ed egg images (compare Fig. 13 with Fig. 9).
Fig. 9: SIRF'd enhanced resolution (~4.5 km pixel resolution)
A image generated from 1 day of simulated `egg' measurements. Grayscale
range is from -20 to -5.
Fig. 10: SIRF'd enhanced resolution (~4.5 km pixel resolution)
A image generated from 1 day of simulated `slice' measurements.
Grayscale range is from -20 to -5.
Fig. 11: SIRF'd enhanced resolution (~4.5 km pixel resolution)
A image generated from 2 days of simulated `slice' measurements.
Grayscale range is from -20 to -5.
Fig. 12: Synthetic `truth' A image. Grayscale range is from -20
Fig. 13: SIRF'd enhanced resolution (~4.5 km pixel resolution)
A image generated from 2 days of simulated `egg' measurements. Grayscale
range is from -20 to -5.
As expected, using slices rather than eggs improves the effective
resolution of land/ice image produced from Seawinds. SIRF further
improves the images. When slice sigma-0 measurements are used with
SIRF, land/ice images comparable to NSCAT images can be produced.
However, while NSCAT measured sigma-0 over a wide range of incidence
at dual polarization, Seawinds will measure sigma-0 at only one
incidence angle per polarization, and these angles are different.
This will adversely impact the application of Seawinds data for
some applications. For example, the NSCAT ice edge algorithm we
have developed is based on the pol ratio and the incidence angle
dependence of sigma-0 and so can not be used with Seawinds. We are
currently evaluating alternative algorithms.