Surface deformation caused by an intrusion and small eruption during June 17-19, 2007, along the East Rift Zone of Kilauea Volcano, Hawaii, was three-dimensionally reconstructed from radar interferograms acquired by the Advanced Land Observing Satellite (ALOS) phased-array type L-band synthetic aperture radar (SAR) (PALSAR) instrument. To retrieve the 3-D surface deformation, a method that combines multiple-aperture interferometry (MAI) and conventional interferometric SAR (InSAR) techniques was applied to one ascending and one descending ALOS PALSAR interferometric pair. The maximum displacements as a result of the intrusion and eruption are about 0.8, 2, and 0.7 m in the east, north, and up components, respectively. The radar-measured 3-D surface deformation agrees with GPS data from 24 sites on the volcano, and the root-mean-square errors in the east, north, and up components of the displacement are 1.6, 3.6, and 2.1 cm, respectively. Since a horizontal deformation of more than 1 m was dominantly in the north-northwest-south-southeast direction, a significant improvement of the north-south component measurement was achieved by the inclusion of MAI measurements that can reach a standard deviation of 3.6 cm. A 3-D deformation reconstruction through the combination of conventional InSAR and MAI will allow for better modeling, and hence, a more comprehensive understanding, of the source geometry associated with volcanic, seismic, and other processes that are manifested by surface deformation.

The intensity of a Sentinel-1 Terrain Observation with Progressive Scans synthetic aperture radar image is disturbed by additive thermal noise, particularly in the cross-polarization channel. Although the European Space Agency provides calibrated noise vectors for noise power subtraction, residual noise contributions are significant when considering the relatively narrow backscattering distribution of the cross-polarization channel. In this paper, we investigate the characteristics of noise and propose an efficient method for noise reduction based on a three-step correction process comprised of azimuth descalloping, noise scaling and interswath power balancing, and local residual noise power compensation. The core idea is to find the optimal correction coefficients resulting in the most noise-uncorrelated gentle backscatter profile over a homogeneous region and to combine them with the scalloping gain for a reconstruction of the complete 2-D noise field. Denoising is accomplished by subtracting the reconstructed noise field from the original image. The performance improvement in some applications by adopting the denoising procedure shows the effectiveness of the proposed method.