Phase decorrelation hampers the accuracy of distributed scatterer (DS) interferometry (DSI) in high-precision deformation monitoring. While several advanced phase linking (PL) techniques built upon the sample coherence matrix (SCM) have shown effectiveness in enhancing the signal-to-noise ratio (SNR), their performance significantly degrades under suboptimal SCM estimation, particularly in scenarios of fast decorrelation and near-zero coherence levels. This article presents an enhanced sequential phase estimator motivated by the degradation of theoretical accuracy due to pure noise-bearing interferograms in full-stack-exploiting PL schemes. In the proposed estimator, Bayesian ensemble theory is first employed to divide the full-stack data adaptively into ministacks based on the coherence pattern, rather than constant-size ministacks usually determined empirically. Building on this, the estimator introduces a nonconvex regularization-based sparse SCM by constraining the coherence matrix to have potential sparsity and low-rank (LR), which suppresses the influence of noisy interferometric pairs and improves SCM estimation. Moreover, we incorporate the deformation elasticity theory to provide additional spatial constraints on the reconstructed phase time series across adjacent pixels, realized by using the strain model to reduce phase discontinuity and further enhance the SNR. Experiments on the simulated and real Sentinel-1 images over a landslide-prone area in western Guizhou, China, demonstrate the effectiveness and superior performance of the new method.

Synthetic aperture radar Interferometry (InSAR) has proven to be an effective landslide monitoring technique, especially for very slow landslides without observable morphological features. Integration of multi-source InSAR observations from different satellites/tracks could help reduce omissions and misjudgements of potential landslides, showing a promising trend toward automatic landslide detection and monitoring at regional or national scale. However, existing methods present poor error suppression performance, and are not capable of describing and solving potential information conflicts, due to the neglect of observation uncertainties during the integration. Here we propose a new integrated method based on Dempster-Shafer evidence theory to address these deficiencies. The two key steps are multi-source InSAR integration based on DST and a two-step decision rule. We apply the proposed method to the entire Jinsha River Basin using both ascending and descending Sentinel-1 SAR data from 2014 to 2023. The preliminary result on the Luoshui-Baini section based on two sources (ascending and descending orbits) identified 68 landslides, which is nearly the same between our method and the existing mosaic method.

How to improve the phase signal-to-noise ratio (SNR) of distributed scatterers (DSs) is a key topic in DS interferometry (DSI). Although some state-of-the-art phase linking (PL) estimators have been proposed, their performance is still limited by the accuracy of the estimated sample covariance matrix (SCM). The key challenges arise from the biased estimation of the near-zero coherence matrix (the magnitude matrix of SCM) under conditions of small sample sizes and heterogeneous samples. To overcome this limitation, we present a sparse regularization-based PL estimator that considers the potential sparsity structure of the inverse covariance matrix. In this new estimator, we first introduced the graphical lasso (GLasso) algorithm into the small samples estimation problem of SCM, which suppresses the biased estimation of the sparse inverse covariance matrix by introducingL1-norm regularization, significantly reducing the impact of weakly coherent interferograms in fast decorrelation scenarios. Furthermore, we also attempt to generalize this scheme to long-term coherence cases through the utilization ofL2-norm regularization. Both synthetic data tests and real Sentinel-1 data covering Changi Airport, Singapore, demonstrate the validity of the proposed approach.

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.

Time series analysis of synthetic aperture radar (SAR) and interferometric SAR generally starts with coregistration for the precise alignment of the stack of images. Here, we introduce a model-adjusted geometrical image coregistration (MAGIC) algorithm for stack coregistration. This algorithm corrects for atmospheric propagation delays and known surface motions using existing models and ensures simplicity and computational efficiency in the data processing systems. We validate this approach by evaluating the impact of different geolocation errors on stacks of the C-band Sentinel-1 and L-band ALOS-2 data, with a focus on the ionosphere. Our results show that the impact of the ionosphere dominates Sentinel-1 ascending (dusk-side) orbit and ALOS-2 data. After correcting for ionosphere using the JPL high-resolution global ionospheric maps, with topside total electron content (TEC) estimated from GPS receivers onboard the Sentinel-1 platforms, solid Earth tides, and troposphere, the mis-registration RMSE reduces by over a factor of four from 0.20 to 0.05 m for Sentinel-1 and from 2.66 to 0.56 m for ALOS-2. The results demonstrate that for Sentinel-1, the MAGIC approach is accurate enough in the range direction for most applications, including interferometry; while for the L-band SAR, it can be potentially accurate enough if topside TEC is available. Based on our current understanding of different error sources, we evaluate the expected range geolocation error budget for the upcoming NISAR mission with an upper bound of the relative geolocation error of 1.3 and 0.2 m for its L- and S-band SAR, respectively.

An active volcano deforms even during its dormant period, as well as its active period, although the deformation during its dormant period has paid less attention than its active period. Here we investigated the deformation of Asama and Usu volcanoes, Japan, during their dormant periods by Synthetic Aperture Radar Interferometry. We found localized deformation in both of these volcanoes, some of which decay over time. In Usu volcano, we interpret the observed deformation as the thermal contraction of the intruded lava dome associated with previous eruptions. In Asama volcano, the observed deformation cannot be due to thermal contraction as with Usu volcano but can be interpreted as hydrothermal activity and flank instability.

In general, a search engine is used to obtain information about specified keywords of interest. However, users must go through the list of web pages presented by the search engine in order to find the page that meets the purpose. In order to reduce this burden, we propose Web Index (WIX), a hyperlink generation system that achieves joining information resources on the web. The WIX system replaces keywords that appear in Web documents on browser into hyperlinks to a specific web page group of the user's choice. However, when there are multiple URLs paired up with a keyword, there is a need to choose the web page that meets the user's purpose. In this paper, we propose WIX System and an architecture that decides and presents likely candidates for hyperlink destination based on similarity of URLs and the content of each candidate.

Geohazard Supersites have been founded to stimulate international and intergovenmental efforts to monitor and study geologically interesting sites by establishing a portal to, or ideally open-access to, relevant datasets. Integrating various geophysical data gives us deeper insights into the mechanics of earthquakes, volcano eruptions, and other geological hazards. Here I overview how the remote sensing data contributes to understand such geological hazards in the Japanese islands, one of the most earthquake- and volcanic eruption-prone regions, by combining with other geophysical datasets.