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Enhanced Resolution Product FAQ

This page contains frequently answered questions regarding the BYU MERS enhanced resolution scatterometer image products.

Why an enhanced resolution product?
What are the available products?
What is the resolution of the images?
How are the enhanced resolution image products produced?
What data is used to make the products
What about time/azimuth variations?
What is the file format used for the products?
Where is the full documentation?

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 and SeaWinds 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.


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 and SeaWinds 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 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?

Detailed QuikSCAT (2.3 MB pdf) documentation is available wotj additional documentation on other sensors here. 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.