Several techniques for detecting temporal changes in satellite synthetic aperture radar ( S A R ) imagery are compared using both theoretical predictions and spaceborne S A R data collected by the European first Remote Sensing Satellite, ERS-1. In a first set of techniques, changes are detected based on differences in the magnitude of the signal intensity between two dates. Ratioing of the multidate radar intensities is shown to be better adapted to the statistical characteristics of S A R data than subtracting, and works best when the number of looks is large. In a second set of techniques, changes are detected based on estimates of the temporal decorrelation of speckle. This method works best with one-look complex amplitude data, but can also be used with intensity data provided that the number of looks is small. The two techniques, ratioing of the signal intensity and decorrelation of speckle, are compared using actual S A R data collected by ERS-1. The results illustrate the viability as well as the complementary character of these techniques for detecting changes in the structural and dielectric properties of remotely sensed surfaces. Finally, change detection using the ratio method is applied to mosaics of repeat-pass S A R imagery to illustrate the potential of this method for monitoring applications at the regional scale.

This paper formulates and demonstrates methods for extracting vegetation characteristics and underlying ground surface topography from interferometric synthetic aperture radar (INSAR) data. The electromagnetic scattering and radar processing, which produce the INSAR observations, are modeled, vegetation and topographic parameters are identified for estimation, the parameter errors are assessed in terms of INSAR instrumental performance, and the parameter estimation is demonstrated on INSAR data and compared to ground truth. The fundamental observations from which vegetation and surface topographic parameters are estimated are (1) the cross-correlation amplitude, (2) the cross-correlation phase, and (3) the synthetic aperture radar (SAR) backscattered power. A calculation based on scattering from vegetation treated as a random medium, including the effects of refractivity and absorption in the vegetation, yields expressions for the complex cross correlation and backscattered power in terms of vegetation characteristics. These expressions lead to the identification of a minimal set of four parameters describing the vegetation and surface topography: (1) the vegetation layer depth, (2) the vegetation extinction coefficient (power loss per unit length), (3) a parameter involving the product of the average backscattering amplitude and scatterer number density, and (4) the height of the underlying ground surface. The accuracy of vegetation and ground surface parameters, as a function of INSAR observation accuracy, is evaluated for aircraft INSAR, which is characterized by a 2.5-m baseline, an altitude of about 8 km, and a wavelength of 5.6 cm. It is found that for •0.5% accuracy in the INSAR normalized cross-correlation amplitude and •5 ø accuracy in the interferometric phase, few-meter vegetation layer depths and ground surface heights can be determined from INSAR for many types of vegetation layers. With the same observational accuracies, extinction coefficients can be estimated at the 0.1-dB/m level. Because the number of parameters exceeds the number of observations for current INSAR data sets, external extinction coefficient data are used to demonstrate the estimation of the vegetation layer depth and ground surface height from INSAR data taken at the Bonanza Creek Experimental Forest in Alaska. This demonstration shows approximately 5-m average ground truth agreement for vegetation layer depths and ground-surface heights, with a clear dependence of error on stand height. These errors suggest refinements in INSAR data acquisition and analysis techniques which will potentially yield few-meter accuracies. The information in the INSAR parameters is applicable to a variety of ecological modeling issues including the successional modeling of forested ecosystems.

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We have converted a conventional synthetic aperture radar system into an imaging radar polarimeter by employing two orthogonally polarized antennas and recording both the amplitude and absolute phase measurements of the received electric fields. This enables us to measure the complete complex scattering matrix for each individual resolution element in the radar image. The Stokes matrix, derived from the scattering matrix, is then used to synthesize the scattering coefficient for any desired combination of transmit and receive antenna polarizations, allowing us to measure the variation of the scattering coefficient with polarization. This polarization information can be displayed in a new and useful way, producing the polarization signature for each point in an image. Comparison of the theoretical and observed polarization signatures permits identification of the dominant scattering mechanisms contributing to the measured radar backscatter. For example, the ocean and smoother lava flows have polarization signatures which are similar to those predicted by a slightly rough dielectric surface model; thus the model likely approximates the physical mechanisms responsible for the reflected energy from these surfaces. Urban areas, on the other hand, exhibit polarization signatures which resemble those predicted by dihedral corner reflector geometries. The coefficient of variation, a parameter derived from the polarization signature, is a measure of resolution scale variation of a surface and indicates the degree to which polarization filtering can be used to enhance or suppress the return from a collection of scatterers. Our observations show that the ocean has a low coefficient of variation; thus the return from the ocean is highly polarized, and polarization effects can be used successfully to suppress or enhance the return from the ocean. Parks and other vegetated areas exhibit a large coefficient of variation, suggesting that the return from these areas contains a considerable component that is randomly polarized; varying the transmit and receive polarizations will not change the return from the vegetated areas appreciably.