We present a modification to the adaptive Goldstein radar interferogram filter which improves the quality of interferometry products. The proposed approach makes the Goldstein filter parameter alpha dependent on coherence, such that incoherent areas are filtered more than coherent areas. This modification minimizes loss of signal while still reducing the level of noise.Index Terms-Phase filtering, phase statistics, radar interferometry, synthetic aperture radar (SAR).

In this paper, a functional model for determining the minimum and maximum detectable deformation gradient in terms of coherence for synthetic aperture radar (SAR) sensors is presented. The model is developed based on a new methodology that incorporates both real and simulated data. Sets of representative surface deformation models have been simulated, and the associated phase from these models introduced into real SAR data acquired by European Remote Sensing 1 and 2 satellites. Subsequently, interferograms were derived, and surface deformation was estimated. A number of cases of surface deformation with varying magnitudes and spatial extent have been simulated. In each case, the resultant surface deformation has been compared with the "true" surface deformation as defined by the deformation model. Based on these comparisons, a set of observations that lead to a new functional model has been established. Finally, the proposed model has been validated against external datasets and proven viable. Although the major weakness of the model is its reliance on visual interpretation of interferograms, this model can serve as a decision-support tool to determine whether or not to apply satellite radar interferometry to study a given surface deformation.

A Synthetic [simulated] Earth Gravity Model (SEGM) of the geoid, gravity and topography has been constructed over Australia specifically for validating regional gravimetric geoid determination theories, techniques and computer software. This regional high-resolution (1-arc-min by 1-arc-min) Australian SEGM (AusSEGM) is a combined source and effect model. The long-wavelength effect part (up to and including spherical harmonic degree and order 360) is taken from an assumed errorless EGM96 global geopotential model. Using forward modelling via numerical Newtonian integration, the short-wavelength source part is computed from a high-resolution (3-arc-sec by 3-arc-sec) synthetic digital elevation model (SDEM), which is a fractal surface based on the GLOBE v1 DEM. All topographic masses are modelled with a constant mass-density of 2670 kg/m 3. Based on these input data, gravity values on the synthetic topography (on a grid and at arbitrarily distributed discrete points) and consistent geoidal heights at regular 1-arcmin geographical grid nodes have been computed. The precision of the synthetic gravity and geoid data (after a first iteration) is estimated to be better than 30 µGal and 3 mm, respectively, which reduces to 1 µGal and 1 mm after a second iteration. The second iteration accounts for the changes in the geoid due to the superposed synthetic topographic mass distribution. The first iteration of AusSEGM is compared with Australian gravity and GPS-levelling data to verify that it gives a realistic representation of the Earth's gravity field. As a by-product of this comparison, AusSEGM gives further evidence of the north-south-trending error in the Australian Height Datum. The freely available AusSEGM-derived gravity and SDEM data, included as Electronic Supplementary Material (ESM) with this paper, can be used to compute a geoid model that, if correct, will agree exactly with the AusSEGM geoidal heights, thus offering independent verification of theories and numerical techniques used for regional geoid modelling.

AUSGeoid2020 is a combined gravimetric-geometric model (sometimes called a "hybrid quasigeoid model") that provides the separation between the Geocentric Datum of Australia 2020 (GDA2020) ellipsoid and Australia's national vertical datum, the Australian Height Datum (AHD). This model is also provided with a location-specific uncertainty propagated from a combination of the levelling, GPS ellipsoidal height and gravimetric quasigeoid data errors via least squares prediction. We present a method for computing the relative uncertainty (i.e. uncertainty of the height between any two points) between AUSGeoid2020-derived AHD heights based on the principle of correlated errors cancelling when used over baselines. Results demonstrate AUSGeoid2020 is more accurate than traditional third-order levelling in Australia at distances beyond 3 km, which is 12 mm of allowable misclosure per square root km of levelling. As part of the above work, we identified an error in the gravimetric quasigeoid in Port Phillip Bay (near Melbourne in SE Australia) coming from altimeter-derived gravity anomalies. This error was patched using alternative altimetry data.