David G. Long

Spaceborne radar measurement of wind velocity over the ocean-an overview of the NSCAT scatterometer system

Scatterometry and scatterometer design issues are reviewed. The design of the NASA Scatterometer (NSCAT) to be flown on the Japanese ADEOS mission is presented. Building on Seasat experience, the NSCAT system includes several enhancements, such as three antenna azimuths in each of two swaths, and an onboard digital Doppler processor to allow backscatter measurements to be colocated everywhere within the orbit. These enhancements will greatly increase the quality of the NSCAT wind data. The ground processing of data is discussed, and scatterometers of the next decade are briefly described. >

Image reconstruction and enhanced resolution imaging from irregular samples

While high resolution, regularly gridded observations are generally preferred in remote sensing, actual observations are often not evenly sampled and have lower-than-desired resolution. Hence, there is an interest in resolution enhancement and image reconstruction. This paper discusses a general theory and techniques for image reconstruction and creating enhanced resolution images from irregularly sampled data. Using irregular sampling theory, we consider how the frequency content in aperture function-attenuated sidelobes can be recovered from oversampled data using reconstruction techniques, thus taking advantage of the high frequency content of measurements made with nonideal aperture filters. We show that with minor modification, the algebraic reconstruction technique (ART) is functionally equivalent to Grochenigs (1992) irregular sampling reconstruction algorithm. Using simple Monte Carlo simulations, we compare and contrast the performance of additive ART, multiplicative ART, and the scatterometer image reconstruction (SIR) (a derivative of multiplicative ART) algorithms with and without noise. The reconstruction theory and techniques have applications with a variety of sensors and can enable enhanced resolution image production from many nonimaging sensors. The technique is illustrated with ERS-2 and SeaWinds scatterometer data.

Resolution enhancement of spaceborne scatterometer data

A method for generating enhanced resolution radar images of the Earths surface using spaceborne scatterometry is presented. The technique is based on an image reconstruction technique that takes advantage of the spatial overlap in scatterometer measurements made at different times to provide enhanced imaging resolution. The reconstruction algorithm is described, and the technique is demonstrated using both simulated and actual Seasat-A Scatterometer (SASS) measurements. The technique can also be used with ERS-1 scatterometer data. The SASS-derived images, which have approximately 4-km resolution, illustrate the resolution enhancement capability of the technique, which permits utilization of both historic and contemporary scatterometer data for medium-scale monitoring of vegetation and polar ice. The tradeoff between imaging noise and resolution inherent in the technique is discussed. >

A median-filter-based ambiguity removal algorithm for NSCAT

A description is given of the baseline NSCAT (the NASA scatterometer) ambiguity removal algorithm and the method used to select the set of optimum parameter values. An extensive simulation of the NSCAT instrument and ground data processor provides a means of testing the resulting tuned algorithm. This simulation generates the ambiguous wind-field vectors expected from the instrument as it orbits over a set of realistic mesoscale wind fields. The ambiguous wind field is then de-aliased using the median-filter-based ambiguity removal algorithm. Performance is measured by comparison of the selected wind fields with the true wind fields. Results have shown that this median-filter-based ambiguity removal algorithm satisfies NSCAT mission requirements, and it therefore has been incorporated into the baseline geophysical data-processing system for NSCAT. >

Spatial resolution enhancement of SSM/I data

One of the limitations in using Special Sensor Microwave/Imager (SSM/I) data for land and vegetation studies is the relatively low-spatial resolution. To ameliorate this limitation, resolution-enhancement algorithms can be applied to the data. In this paper, the Backus-Gilbert inversion (BGI) technique and the scatterometer image-reconstruction (SIR) algorithm are investigated as possible methods for creating enhanced resolution images from SSM/I data. The two algorithms are compared via both the simulation and the actual SSM/I data. The algorithms offer similar resolution enhancement, though SIR requires significantly less computation. Sample results over two land regions of South America are presented.

Azimuth variation in microwave scatterometer and radiometer data over Antarctica

While designed for ocean observation, scatterometer and radiometer data have proven very useful in a variety of cryosphere studies. Over large regions of Antarctica, ice sheet and bedrock topography and the snow deposition, drift, and erosional environment combine to produce roughness on various scales. Roughness ranges from broad, basin-scale ice-sheet topography at /spl sim/100 km wavelengths to large, spatially coherent dune fields at /spl sim/10 km wavelength to erosional features on the meter scale known as sastrugi. These roughness scales influence the microwave backscattering and emission properties of the surface, combining to introduce azimuth-angle dependencies in the satellite observation data. In this paper, the authors explore the use of NASA scatterometer (NSCAT) data, European remote sensing (ERS) advanced microwave instrument (AMI) scatterometer mode data, and special sensor microwave/imager (SSM/I) data to study surface roughness effects in Antarctica. All three sensors provide strong evidence of azimuth modulation, which is correlated with the surface slope environment and results in a katabatic wind flow regime. Due to its broad azimuth coverage, NSCAT data appears to be the best suited for azimuth-angle observations. A simple empirical model for the azimuth variation in the radar backscatter is developed, and an algorithm for computing the parameters of the model from NSCAT data at a fine scale is presented. Results indicate relationships exist between the azimuthal variation of the data and the orientation of the surface slope and small-scale roughness relative to the sensor-look direction.

Sea ice extent mapping using Ku band scatterometer data

Although spaceborne scatterometers such as the NASA scatterometer have inherently low spatial resolution, resolution enhancement techniques can be used to increase the utility of scatterometer data in monitoring sea-ice extent in the polar regions, a key parameter in the global climate. The resolution enhancement algorithm produces images of A and B, where A is the normalized radar backscatter coefficient σO at 40° incidence and B is the incidence angle dependence of σO. Dual-polarization A and B parameters are used to identify sea ice and ocean pixels in composite images. The A copolarization ratio and vertically polarized B are used as primary classification parameters to discriminate between sea ice and open ocean. Estimates of the sea-ice extent are obtained using linear and quadratic (Mahalanobis distance) discriminant boundaries. The distribution parameters needed for the quadratic estimate are taken from the linear estimate. The σO error variance is used to reduce errors in the linear and Mahalanobis ice/ocean classifications. Noise reduction is performed through binary image region growing and erosion/dilation techniques. The resulting edge closely matches the NASA Team algorithm special sensor microwave imager derived 30% ice concentration edge. A 9-month data set of global sea-ice extent maps is produced with one 6-day average map every 3 days.

Global ice and land climate studies using scatterometer image data

Scatterometers have provided continuous synoptic microwave radar coverage of the Earth from space for nearly a decade. NASA launched three scatterometers: the current SeaWinds scatterometer onboard QuikSCAT (QSCAT, 13.4 GHz) launched in 1999; the NASA scatterometer (NSCAT, 14.0 GHz), which flew on the Japanese Space Agencys ADEOS-1 platform during 1996–1997; and the Seasat-A scatterometer system (SASS, 14.6 GHz), which flew in 1978. The European Space Agencys (ESA) 5.3-GHz scatterometer (ESCAT) has been carried onboard both the ERS-1 and ERS-2 satellites since 1991. properties, including the phase state, of a particular surface type. Varying response from the surface also results from different polarizations, viewing angles and orientations, and radar frequencies. The wide swath of scatterometers provides near daily global coverage at intrinsic sensor resolutions that are generally between 25–50 km.

Greenland snow accumulation estimates from satellite radar scatterometer data

Data collected by the C band ERS-2 wind scatterometer (EScat), the Ku band ADEOS-1 NASA scatterometer (NSCAT), and the Ku band SeaWinds on QuikScat (QSCAT) satellite instruments are used to illustrate spatiotemporal variability in snow accumulation on the Greenland ice sheet. Microwave radar backscatter images of Greenland are derived using the scatterometer image reconstruction (SIR) method at 3-day intervals over the periods 1991–1998 and 1996–1997 for EScat and NSCAT, respectively. The backscatter coefficient σ° normalized to 40° incidence, A, and gradient in backscatter, B, in the range 20°–60° are compared with historical snow accumulation data and recent measurements made in the Program for Arctic Regional Climate Assessment (PARCA) shallow snow pits. Empirical relationships derived from these comparisons reveal different exponential relationships between C and Ku band A values and dry snow zone mean annual accumulation, Q. Frequency difference images between overlapping scatterometer images suggest that C band data are more sensitive to snow layering and buried inhomogeneities, whereas Ku band data are more sensitive to volume scattering from recently accumulated snow. Direct comparisons between NSCAT B values and in situ Q measurements show a linear relationship between ln (Q) and B, with a negative rank correlation of R = −0.8. The root-mean-square residual in fitting regression line equation ln (Q) = 3.08 − 17.83B to the data is 0.05-m snow water equivalent. This simple Ku band empirical relationship is exploited to investigate decadal changes in dry snow zone accumulation between Seasat (1978) and NSCAT (1996). Additional comparisons between NSCAT and recent QSCAT (1999) data reveal significant upslope shifts in the dry snow line along the southwestern flank of the ice sheet. Recent acceleration in the increase in intensity of scattering is observed in the percolation zone, suggesting increased melting between 2000- and 3000-m elevation in the southern half of the ice sheet.

Vegetation studies of the Amazon basin using enhanced resolution Seasat scatterometer data

The Seasat-A scatterometer (SASS) was designed to measure the near-surface wind field over the ocean by inferring the wind from measurements of the surface radar backscatter. While backscatter measurements were also made over land, they have been primarily used for the calibration of the instrument. This has been due in part to the low resolution of the scatterometer measurements (nominally 50 km). In a separate paper the present authors introduced a new method for generating enhanced resolution radar measurements of the Earths surface using spaceborne scatterometry. In the present paper, the method is used with SASS data to study vegetation classification over the extended Amazon basin using the resulting medium-scale radar images. The remarkable correlation between the Ku-band radar images and vegetation formations is explored, and the results of several successful experiments to classify the general vegetation classes using the image data are presented. The results demonstrate the utility of medium-scale radar imagery in the study of tropical vegetation and permit utilization of both historic and contemporary scatterometer data for studies of global change. Because the scatterometer provides frequent, wide-area coverage at a variety of incidence angles, it can supplement higher resolution instruments which often have narrow swaths with limited coverage and incidence angle diversity. >