As a supplement to synthetic aperture radar interferometry (InSAR), multiple-aperture InSAR (MAI) can measure along-track surface deformation, but it is limited by ionospheric path delays, especially with the L- or P-band data. In this letter, we propose a method to correct an ionospheric error in the MAI measurement based on the along-track gradient and 1-D linear fitting. The method depends on the uniqueness of the spatial variation of the along-track gradient of ionospheric error in MAI measurements, which can be well distinguished from other components, such as deformation by using 1-D linear fitting. The method is first evaluated by employing the L-band ALOS-2 PALSAR-2 dataset of the 2019 Ridgecrest earthquake, U.S., and then applied to estimate glacial movements of Grove Mountain, Antarctica, with the ALOS-2 PALSAR-2 dataset.

Isolating the orbital error from the interferometric synthetic aperture radar (InSAR) observations is a great challenge, especially in the presence of tectonic deformation due to their similar spatial patterns. The influence of orbital error is systematic, which can reduce the reliability of deformation monitoring. In this letter, we propose a method to isolate the orbital error from the multitemporal InSAR (MTInSAR) derived tectonic deformation based on the wavelet multiresolution analysis and independent component analysis (ICA). Starting from the sequential interferometric phase of unwrapping, the tectonic deformation and orbital error are firstly extracted from the interferometric phase by wavelet analysis based on their longwavelength spatial patterns, and ICA is then used to isolate the orbital error from the tectonic deformation according to the different temporal characteristics of the two types of signals. In the simulation experiment, the root-mean-square error (RMSE) of the isolated orbital error is 2.6 mm. Experiments with real data in Southern California show that the proposed method can successfully separate the orbital error from the tectonic deformation, and the InSAR deformation rates are in good agreement with the GPS observations.

With the increasing amount of synthetic aperture radar (SAR) data with various imaging geometries (at least ascending/descending tracks), it is possible to obtain accurate multi-dimensional (MD) deformation time series with long time span. However, in most cases SAR data of different geometries are un-synchronously acquired over the same region, making it impossible to directly solve the underdetermined observation model (OSM) between the interferometric SAR (InSAR) measurements and the MD deformations. Kalman filter (KF), as one of the most famous dynamic estimators, can obtaina prioriinformation of the unknowns based on the preexisting time series, therefore it can be used to deal with this InSAR underdetermined problem. This article employs the KF to realize the dynamic estimation of MD deformations with short-baseline interferograms. The innovation lies in the establishment of the KF state transition model (STM) and OSM, which aims to make the InSAR monitoring problem better adapt to the KF. Particularly, by assuming a smooth deforming process, existing deformation time series are used to establish the STM and to predict the deformations at current moment. Besides, a strain model (SM) is employed to assist the establishment of the OSM. Simulation and real experiments in the Geysers geothermal field (GGF), U.S. demonstrate that, compared with the state-of-the-art methods, the proposed KF method allows more robust deformation estimation and achieves higher computational efficiency for dynamic estimation.

Time-series SAR interferometry (TS-InSAR) has been widely used to map the millimeter-scale interseismic displacements. Tropospheric noise is still a key error source that hinders further improvement of such measurement. In this article, a new spatiotemporal TS-InSAR tropospheric noise filtering is proposed to approach higher accuracy measurements. We first construct a 2-D arc network for all the data points, and based on that a temporal high-pass and spatial low-pass filtering is applied to estimate the tropospheric noise for all the points. To better filter out long-wavelength interseismic displacements in temporal high-pass filtering, we construct four-candidate time-series models to model the displacement histories for each arc. To avoid overfitting, the F hypothesis test is applied to select the most suitable model for all arcs. Notably, instead of the commonly used Delaunay network, the all-pairs-shortest-path algorithm in the graph theory is employed to reconstruct all arcs and to improve the applicability of the time-series model. Comprehensive tests using synthetic data and Sentinel-1 data covering Central San Andreas Fault (CSAF) creep section validate our tropospheric noise removal approach in measuring the interseismic velocity across the fault.

How to obtain millimeter-scale along-track deformations using phase measurements is still a pending question for InSAR community. Although Burst Overlap Interferometry (BOI) technique makes this question seem tractable, most applications of BOI still focus on extracting centimeter-scale deformations for co-seismic cases. To further improve measurement accuracy, here we propose a new time series BOI algorithm towards maximizing the performance of BOI and obtaining as high accuracy of the along-track deformations as possible. This algorithm has three major steps. The first step is to enhance BOI phase signal-to-noise ratio using our newly proposed phase estimator. In the second step, we apply a strain model-based method to further suppress the phase noise and rescue more data points. In the third step, a misregistration correction procedure which considers plate motion is applied to mitigate BOI time series bias. We tested our proposed algorithm over the Chaman fault. Although the derived millimeter-scale deformations demonstrate the effectiveness of our method, experimental results show that decorrelation and ionospheric disturbance are still two great challenges of BOI techniques.

Interferometric synthetic aperture radar (InSAR) technique is a proven technique for measuring 3-D surface deformations by combining InSAR measurements from different techniques (i.e., differential InSAR, multiaperture InSAR, and pixel offset-tracking) and different tracks (i.e., ascending and descending) on a pixel-by-pixel basis. However, it is difficult to obtain the exact a priori variances or weights for such different kinds of InSAR measurements, resulting in inaccurate estimations of 3-D deformations. This paper proposes a method to retrieve 3-D deformations with InSAR by integrating the strain model and variance component estimation algorithm, which can exploit the spatial correlation of the adjacent points' deformations and produce accurate weights for multiple InSAR measurements. The proposed method is assessed with both simulated and real data sets. The results have shown that the proposed method can accurately measure 3-D surface deformations associated with geohazards, and even those occurring in a transient or short-term period (e.g., earthquake and volcanic eruption). In the case study of the 2007 eruption of Kilauea Volcano (Hawai'i), improvements of 51.2%, 22.4%, and 18.5% have been achieved for the derived east, north, and up displacements, respectively, with respect to those derived from the classical weighted least squares method.