Diane L. Evans

Radar polarimetry: analysis tools and applications

The authors have developed several techniques to analyze polarimetric radar data from the NASA/JPL airborne SAR for Earth science applications. The techniques determine the heterogeneity of scatterers with subregions, optimize the return power from these areas, and identify probable scattering mechanisms for each pixel in a radar image. These techniques are applied to the discrimination and characterization of geologic surfaces and vegetation cover, and it is found that their utility varies depending on the terrain type. It is concluded that there are several classes of problems amenable to single-frequency polarimetric data analysis, including characterization of surface roughness and vegetation structure, and estimation of vegetation density. Polarimetric radar remote sensing can thus be a useful tool for monitoring a set of Earth science parameters. >

Overview of results of Spaceborne Imaging Radar-C, X-Band Synthetic Aperture Radar (SIR-C/X-SAR)

The Spaceborne Imaging Radar-C, X-Band Synthetic Aperture Radar (SIR-C/X-SAR) was launched on the Space Shuttle Endeavour for two ten day missions in the spring and fall of 1994. Radar data from these missions are being used to better understand the dynamic global environment. During each mission, radar images of over 300 sites around the Earth were obtained, returning over a terabit of data. SIR-C/X-SAR science investigations were focused on quantifying radars ability to estimate surface properties of importance to understanding global change; and focused studies in geology, ecology, hydrology and oceanography, as well as radar calibration and electromagnetic theory studies. In addition, the second flight featured an interferometry experiment, where digital elevation maps were obtained by interfering data from the first and second shuttle flight, and from successive days on the second flight. SIR-C/X-SAR data have been used to validate algorithms which produce maps of vegetation type and biomass; snow, soil and vegetation moisture; and the distribution of wetlands, developed with earlier aircraft data. >

Multipolarization Radar Images for Geologic Mapping and Vegetation Discrimination

The NASA/JPL airborne synthetic aperture radar system produces radar image data simultaneously in four linear polarizations (HH, VV, VH, HV) at 24.6-cm wavelength (L-band), with 10-m resolution, across a swath width of approximately 10 km. The signal data are recorded optically and digitally and annotated in each of the channels to facilitate a completely automated digital correlation. Both standard amplitude, and also phase difference images are produced in the correlation process. Individual polarization and range-dependent gain functions improve the effective dynamic range, but as yet do not permit absolute quantitative measurements of the scattering coefficients. However, comparison of the relative intensities of the different polarizations in individual black-and-white and color composite images provides discriminatory mapping information. In the Death Valley, California, area, rough surfaces of young alluvial deposits produce strong responses at all polarizations. Smoother surfaces of older alluvial deposits show significantly lower responses. Evaporite deposits of different types and moisture contents have distinct polarization signatures. In the Wind River Basin, Wyoming, sedimentary rock units show polarization responses that relate to differences in weathering. Local intensity variations in like-polarization images result from topographic effects; strong cross-polarization responses denote the effects of vegetation cover and, in some cases, possible scattering from the subsurface. In the Savannah River Plant, South Carolina, forest cover characteristics are discriminated by polarization responses that reflect the density and structure of the canopy, and the presence or absence of standing water beneath the canopy.

Estimates of surface roughness derived from synthetic aperture radar (SAR) data

Radar remote sensing data provide a unique perspective on the Earths crust and the processes that have influenced its evolution. Physically based models are required, however, to relate the geophysical quantities being measured by the radar sensor to useful geologic information. Synthetic aperture radar (SAR) data over the Cima volcanic field in the Mojave Desert of California are quantitatively connected with microtopography through inversion of a radar backscatter model. Changes in surface roughness inferred from the derived microtopography are modeled and found to be consistent with aeolian mantling as surfaces age. Estimated rates of aeolian deposition for the Cima area are compared to the Lunar Crater volcanic field in Nevada. Rates of deposition appear to be higher at Cima volcanic field, most likely because of its proximity to Soda Lake, the main source of the aeolian material. >

Overview of the Spaceborne Imaging Radar-C / X-band Synthetic Aperture Radar (SIR-C/X-SAR) Missions

Abstract The Spaceborne Imaging Radar-C/X-band Synthetic Aperture Radar (SIR-C/X-SAR), the most advanced imaging radar system to have flown in Earth orbit, was carried in the cargo bay of the Space Shuttle Endeavour in April and October 1994. SIR-C/X-SAR simultaneously recorded data at three wavelengths (L-, C-, and X-bands; 23.5 cm, 5.8 cm, and 3.1 cm, respectively). In addition, the full polarimetric scattering matrix was obtained at L- and C-band over a variety of terrain and vegetation types. Scientists are using multifrequency, polarimetric SIR-C/X-SAR data in studies of geology, hydrology, ecology, oceanography, and radar remote sensing techniques. The October SIR-C/X-SAR flight also included acquisition of experimental repeat-pass interferometry data which have been used to generate digital elevation models and to detect surface motions in volcanic, tectonic, and glacial terrains. Results from SIR-C/X-SAR clearly show the increased value of using multiparameter and interferometric capabilities to characterize Earths surface and vegetation cover and to generate geophysical products compared with optical sensors or single-channel radars alone.

Data fusion for investigating land subsidence and coal fire hazards in a coal mining area

Coal mining areas all over the world are often threatened by serious environmental hazards such as the occurrence of coal fires, land subsidence, etc. Coal fires burn away the natural non-renewable coal resources, locally raise the temperature of the area, emit polluting gases such as oxides of carbon, sulphur and nitrogen, and when present underground are even the cause of land subsidence. Mining-induced subsidences, on the other hand, cause horizontal and vertical movements in the land surface, and open cracks and fissures that serve as inlets for oxygen, which in turn aggravate the problem of coal fires. These inter-related phenomena often render the mining areas unfit for human inhabitation and the commercial exploitation of coal nearly impossible in some parts. In this study, satellite data acquired in three regions of the electromagnetic spectrum, namely optical, thermal and microwave, along with field data, are used to identify the areas affected by coal fires and land subsidence in a coalfield in north-west China. Data fusion techniques are used for an integrated analysis of this complex problem.

Approach to derivation of SIR-C science requirements for calibration

An attempt at determining the science requirements for polarimetric calibration for SIR-C is reported. A model describing the effect of miscalibration is presented, followed by an example showing how to assess the calibration requirements specific to an experiment. The effects of miscalibration on some commonly used polarimetric parameters are also discussed. It is shown that polarimetric calibration requirements are strongly application dependent. In consequence, the SIR-C investigators are advised to assess the calibration requirements of their own experiment. A set of numbers summarizing SIR-C polarimetric calibration goals is given. >

The shuttle imaging radar-C and X-SAR mission

The Shuttle Imaging Radar-C and X-Band Synthetic Aperture Radar (SIR-C/X-SAR) (Figure 1) is a cooperative space shuttle experiment between NASA, the German Space Agency, and the Italian Space Agency. The experiment is the next evolutionary step in NASAs Spaceborne Imaging Radar (SIR) program that began with the Seasat Synthetic Aperture Radar (SAR) in 1978 and continued with SIR-A in 1981 and SIR-B in 1984. It also represents a continuation of Germanys imaging radar program, which started with the Microwave Remote Sensing Experiment flown aboard the Shuttle on the first SPACELAB mission in 1983. The SIR-C/X-SAR mission benefits from synergism with the Magellan mission to Venus, other international spaceborne radar programs, and prototype aircraft sensors such as the Jet Propulsion Laboratorys Airborne SAR (AIRSAR) and the German Aerospace Establishment (DLR) E-SAR.

Radar interferometry studies of the Earth's topography

Topographic information is required for many geological and geophysical investigations. For example, detailed topographic data alone can be used to map geological structure and thus reveal the effects of tectonic deformation. Additionally, they can be combined with gravity field measurements to constrain models of lithospheric structure and rheology [e.g., Topographic Science Working Group, 1988].

Multisensor classification of sedimentary rocks

Abstract This paper compares linear discriminant analysis and supervised classification results based on signatures from the Landsat Thematic Mapper (TM), the Thermal Infrared Multispectral Scanner (TIMS), and airborne Synthetic Aperture Radar (SAR), alone and combined into “extended spectral signatures” for seven sedimentary rock units exposed on the margin of the Wind River Basin, Wyoming. Results from a linear discriminant analysis showed that training-area classification accuracies based on the multisensor data were improved an average of 15% over TM alone, 24% over TIMS alone, and 46% over SAR alone, with similar improvement resulting when supervised multisensor classification maps were compared to supervised, individual sensor classification maps. When training area signatures were used to map spectrally similar materials in an adjacent area, the average classification accuracy improved 19% using the multisensor data over TM alone, 2% over TIMS alone, and 11% over SAR alone. The conclusion of this study is that certain sedimentary lithologies may be accurately mapped using a single sensor, but classification of a variety of rock types can be improved using multisensor data sets that are sensitive to different characteristics such as mineralogy and surface roughness.