Enhanced Resolution Product FAQ
This page contains frequently answered questions regarding the
BYU MERS enhanced resolution scatterometer image products.
Why an enhanced resolution product?
Though originally developed for wind retrieval over the ocean, scatterometer
data has proven very useful in studies of land and ice (e.g., Long and Drinkwater,
1999). However, its low resolution can limit its utility. This limitation can
be amerliorated by the use of resolution enhancement algorithms. BYU has developed
a standard suite of products that, while not applicable to every study, can
have wide application to a variety of studies of land, vegetation, and ice.
What are the available products?
Enhanced resolution products are available on a daily basis in the polar regions
and less frequently gobally. The "standard" image set consists of both SIRF-generated
enhanced resolution backscatter images, as well as non-enhanced images and a
number of auxiliary data images.
What is the resolution of the images?
The pixel resolution varies for different sensors. For ERS-1/2, it is 8.9
km while for SASS and NSCAT it is 4.45 km/pixel. For QuikSCAT the pixel is about
4.45 km/pixel for egg-based images or 2.25 km/pixel for slice-based images.
The effective resolution depends on the sampling and time interval
and is coarser than this, typically by about a factor of 2 or more.
How are the enhanced resolution image products produced?
Resolution enhancement is done using the BYU-developed Scatterometer Image
Reconstruction (SIR) with Filtering (SIRF) algorithm. The SIRF algorithm was
originally developed to enhance Seasat scatterometer image resolution by combining
data from multiple passes of the satellite (Long, Hardin and Whiting, 1993)
but has also be used with SSM/I radiometer data (Long and Daum, 1997) and ERS
scatterometer data (Long, et. al, 1994). A number of improvements to the original
SIRF algorithm have been developed to optimize its performance.
The SIRF algorithm is based on a multivariate form of block multiplicative
algebraic reconstruction. Combining multiple overlapping passes and robust performance
in the presence of noise, it provides enhanced resolution measurements of the
surface characteristics. The method used is a true reconstruction of
the surface response using information in the sidelobes of the measurement resonse
function (Early and Long, 2001).
To provide a simple intuitive explanation of the idea behind SIRF, consider
the following. (The incidence angle dependence of sigma-0 is ignored in the
following discussion.)
Let f(x,y) be a function that gives the surface sigma-0 at a point
(x,y). The scatterometer measurement system can be modeled by
z = H f + noise
where H is an operator that models the measurement system (sample spacing
and aperture filtering) and z represents the measurements made by
the instrument sensor. The set of measurements z are a discrete
sampling of the function f convolved with the aperture function (which may
be different for each measurement). A particular measurement z_i can be
written as
z_i = Integral h_i(x,y) dx dy + noise
where h_i(x,y) is the measurement response function (due, for example,
to the antenna pattern and the Doppler filter response) of the i-th
measurement.
For resolution enhancement, we are interested in the inverse problem:
f_estimate = Inverse(H_estimate) z
where f_estimate is an estimate of f from the measurements z. The inverse of
the operator H is exact only if H is invertible and the
measurements are noise free; otherwise, the result is an approximation
to the original surface.
This represents a form of resolution enhancement since information in the
sidelobes of the measurement response or aperture function is recovered in
the inversion. In effect, this is what iterative SIRF algorithm does,
producing images at a finer resolution than the original measurements.
Thus SIR is a true resolution enhancement algorithm which extracts
information from the sidelobes of the measurement response function to
generate the final image product (Early and Long, 1999)}; in effect, it is
an inverse reconstruction filter optimized to minimize noise in the
reconstructed image.
References:
D.G.Long, P.Hardin, and P.Whiting, "Resolution Enhancement of Spaceborne
Scatterometer Data," IEEE Trans. Geosci. Remote Sens., vol. 31,
pp. 700-715, 1993.
D.G.Long and D.Daum, "Spatial Resolution Enhancement of SSM/I Data,"
IEEE Trans. Geosci. Rem. Sens., vol. 36, pp. 407-417, 1997.
D.G.Long, D.Early, and M.R.Drinkwater, "Enhanced Resolution ERS-1
Scatterometer Imaging of Southern Hemisphere Polar Ice, Proc. Int.
Geosci. Rem. Sens. Sym., Pasadena, California, 8-12 August, pp. 156-158, 1994
D.G.Long and M.R.Drinkwater, "Cryosphere Applications of NSCAT Data,"
IEEE Trans. Geosci. Remote Sens., Vol. 37, No. 3, pp. 1671-1684, 1999.
D.S.Early and D.G.Long,"Image Reconstruction and Enhanced Resolution Imaging
From Irregular Samples," IEEE Trans. Geosci. Remote Sens., Vol. 39,
No.2, pp. 291-302, Feb. 2001.
What data is used to make the products?
The products are produced from raw sigma-0 measurements. For NSCAT this is
L1.5 while for QuikSCAT this is L1B data. Only measurements flagged as 'usable'
are included in the image products. Negative measurements are discarded.
What about time/azimuth variations?
In generating enhanced resolution images, the SIRF algorithm combines sigma-0
measurements (only measurements from a single beam are combined) from multiple
azimuth angles and (possibly) multiple orbit passes collected over the imaging
period. The resulting images represent a non-linear weighted average of the
measurements. There is an implicit assumption that the surface characteristics
remain constant over the imaging period. The effective resolution depends on
the number of measurements and the precise details of their overlap, orientation,
spatial locations.
All sigma-0 measurements (from a single beam) falling within a single pixel
are averaged and thus forward-looking and aft-looking measurements are
averaged. The resulting average is over the various azimuth angles of the
measurements. The azimuth angle sampling varies with pixel location and the
Julian day and may be affected by missing or low-quality data. Swath edge
discontinuities may result in areas of significant azimuth modulation of
sigma-0 at surface.
What is the file format used for the products?
The image products are stored in the BYU MERS SIR file format in which the
image is stored as a scientific (real valued) image that includes both location
and transformation information in a header. Viewer and reader programs for the
BYU MERS SIR file format are available on line from the BYU MERS
web site and MERS ftp site as well
as the NASA Scatterometer Climate Record Pathfinder web
site and ftp site.
A SIR format file consists of one or more 512 byte headers followed by the
image data and additional zero padding to insure that the file is a multiple of
512 bytes long. The file header record contains all of the information
required to read the remainder of the file and the map projection
information required to map pixels to lat/long on the Earth surface.
The image pixel values may be stored in one of three ways. The primary way
is as 2 byte integers (with the high order byte first), though the pixels
may be stored as single bytes or IEEE floating point values. Scale
factors are stored in the header to convert the integer or byte pixel values
to native floating point units.
The sir file header contains other numerical values and strings which describe
the image contents. For example, a no-data flag value is set in the header as
well as a nominal display range and the minimum and maximum representable
value.
The image is stored in row-scanned (left to right) order from the lower left
corner (the origin of the image) up through the upper right corner. By default,
the location of a pixel is identified with its lower-left corner. The origin
of pixel (1,1) is the lower left corner of the image. The array index n
of the (i,j)th pixel where i is horizontal and j is vertical
is given by n=(j-1)*Nx+i where Nx is the horizontal dimension
of the image.
Where is the full documentation?
Documentation is available in either postscript
(441K) or pdf (269K) form. Further information
is available on line from the BYU
MERS web site or the
NASA Scatterometer Climate Record Pathfinder web
site
Note: All BYU-produced data products and associated documentation and software
are copyright BYU. BYU-produced data products may not be used for commercial
purposes without written authorization by Dr. David G. Long (further
authorization may be required from NASA). Appropriate acknowledgement for
BYU MERS and the JPL PO.DAAC
should be given when using data products in published works,
with a copy of the publication sent to Dr. David G. Long and to the
JPL PO.DAAC.
Last revised: 3 March 2001
Send suggestions or comments to mers-info@ee.byu.edu
© 1999,2000,2001 Microwave Earth Remote Sensing (MERS)
Lab, Brigham Young University.
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