Synthetic Aperture Radar Interferometry (InSAR) is utilized to evaluate slope stability, revealing pronounced periodic oscillations in the deformation time series. Although such periodic patterns have conventionally been ascribed to stratified tropospheric delays or seasonal precipitation in prior studies, periodic signals persist in the deformation results even after atmospheric phase removal by a linear iterative model. This observation underscores the limitations of conventional deformation interpretations and prompts further investigation into the underlying physical mechanisms. To address this, an interferometric phase correction model accounting for variations in surface dielectric property has been proposed. This model effectively removes phase delays induced by dielectric property changes from the raw interferometric phase, enabling the extraction of linear trend signals that reflect actual surface deformation. To verify the reliability of the correction model, Sentinel-1A (C-band) and TerraSAR-X (X-band) radar data is employed to quantify deformation patterns in the slope of non-sliding section around the Huangnibazi landslide. The analysis consistently identifies periodic deformation signals in both SAR datasets after mitigating atmospheric influences. Our findings indicate that dielectric property changes constitute a critical factor in InSAR deformation monitoring that cannot be overlooked. The observed periodic fluctuations in deformation time series are attributed to dielectric-induced phase modulation effects. Furthermore, systematic correlation analyses confirm a strong coherence between soil moisture variations and deformation fluctuations, with minimal temporal hysteresis. In contrast, seasonal precipitation exhibits a weaker correlation with deformation and longer hysteresis time. These results robustly support the theoretical framework linking soil moisture, dielectric property, penetration depth, and phase delay. This insight holds significant implications for enhancing the effectiveness of InSAR technology in slope disaster monitoring and early warning systems.

Digital elevation models (DEMs) are essential for national economic development, disaster management, and military applications. Multi baseline interferometric synthetic aperture radar (MB-InSAR) technology has proven to be an effective method for DEM reconstruction. However, the presence of atmospheric noise and other residual signals introduces unavoidable errors in the phase observations, and most MB-InSAR DEMs are generated using a single empirical mathematical model that ignores the influence of deformation factors. To compensate for these limitations, we propose spatial independent component analysis (sICA) phase separation and interferometric synthetic aperture radar (InSAR) combinatorial modeling (CM) InSAR CM (ISCM). The sICA was used for phase separation, resulting in clear InSAR signals and reducing atmospheric noise and other residual signal interference; then, the effects of linear deformation, seasonal deformation, and environmental factors were considered in the InSAR modeling. In the experiments, a total of 19 TerraSAR-X images from San Diego, USA (SD), and 18 PAZ images from Yan’an, China (YA), were selected to generate DEMs with resolutions of 3 and 6 m, respectively. The accuracy of the DEM generated by ISCM was evaluated using the photogrammetric DEM, and the root-mean-square errors (RMSEs) of the elevation are 3.20 m for SD and 4.41 m for YA, with an improvement of 30.8%–44.9% and 21.9%–38.4%, respectively, compared to the traditional MB-InSAR method. In addition, ICESat/GLAS data collected in YA were used for further validation with an improvement of 13.7%–29.5%. The DEM generated by ISCM has significant advantages in improving accuracy and preserving terrain features, providing theoretical support for global high-precision DEM mapping.

Tropospheric delay significantly hinders the accurate acquisition of high-precision surface deformation by Time series Interferometric Synthetic Aperture Radar (InSAR). The main challenge for current InSAR tropospheric delay estimation lies in effectively utilizing the time-dependent characteristics of the tropospheric delay for accurate atmospheric delay estimation. This paper develops a model to estimate the time-dependent and stochastic components of the delay based on periodic and random characteristics. The experiment demonstrates the effectiveness of the proposed method regardless of whether terrain-dependent delays dominate or random delays dominate at Danba-Xiaojin. The Std of the corrected decreases in 89/90 of the interferograms. The average and maximum improvement of Std is more than 40% and 80% respectively. From the time series, the proposed method can effectively suppress the periodic signals in both non-deformation and deformation regions and can obtain smoother time series. Overall, the proposed method outperforms the other four models for InSAR tropospheric delay correction.

This paper compares two techniques for obtaining forest height and vertical structure from synthetic aperture radar (SAR) data, namely SAR tomography (TomoSAR) and phase histogram (PH). The comparison is carried out on an experimental basis by using the tomographic SAR dataset from the TomoSense campaign, flown by ESA in 2020/21 to support investigation of forested area for future low frequency spaceborne SAR missions. Both techniques were tested using data at full and degraded resolution. Experimental results show that the PH technique can only loosely approximate the forest vertical structure produced by SAR tomography, although it was able to achieve a fairly good estimate of forest height if the appropriate range of height of ambiguity is used. A degraded performance of the PH technique when applied to low-resolution data indicates that this technique is best fit for the case of high-resolution data, consistently with the assumption of the presence of dominant scatterers. Overall, these findings indicate the PH technique as an interesting option in the context of high-resolution spaceborne missions.

Synthetic aperture radar tomography (TomoSAR) at different radar wavelength can be used to measure different structural elements of forests. In this letter, we compared the airborne P- and L-band synthetic aperture radar (SAR) tomograms and TomoSAR-measured canopy height model (CHM) and above-ground biomass (AGB) over a tropical forest in Lopé, Gabon. The SAR data sets were acquired by German Aerospace Center (DLR)’s F-SAR system during the AfriSAR2016 campaign. First, the Weighted covariance fitting-based Iterative Spectral Estimator (WISE) was applied to obtain tomograms. CHM was then retrieved based on the canopy phase center derived from the tomograms. Finally, AGB was estimated via an empirical logarithmic model developed from field measurements and the tomographic backscatter power of vegetation layers between 40 and 50 m above ground. Compared with the classical approaches of Capon and Wavelet-based Compressed Sensing, the WISE method can achieve better resolution with higher computational efficiency and reduce the ambiguity level of L-band tomograms successfully. The experimental results also show that there is no substantial difference between P- and L-band TomoCHM, while P-band tomographic intensity is more sensitive than the L-band for the inversion of tropical forest AGB at a resolution of 50 m$\times $50 m.

Three dimensional displacements are more convenient for people understanding ground processes. However, only azimuth or line of sight (LOS) measurements can be directly extracted from SAR images which restrict further applications, especially for people who are not expertise in radar remote sensing. Aiming at this problem, we proposed an approach of retrieving time series three-dimensional displacements from multi-angular SAR datasets. Firstly, time series displacements in the azimuth and LOS direction can be estimated using traditional methods of SAR interferometry (InSAR) and SAR pixel offset tracking. Then, a fitting and interpolation procedure was applied to parameterize the displacement history and interpolate the displacements from different datasets with identical dates. Thus, three dimensional displacements can be obtained by making use of the different observation geometry of different SAR datasets. Our method was applied to retrieve three dimensional displacements history of the Shuping landslide, Three Gorges area, China.

Speckle inherent in synthetic aperture radar (SAR) images usually complicates visual interpretation and brings difficulty to information extraction for applications. Current speckle filters are mainly developed for single SAR image or an image pair (InSAR or PolInSAR). Although some multichannel filters are proposed, they only exploit pixel intensity to identify statistically homogeneous pixels (SHPs). In this paper, we present a new unified approach to filter multitemporal SAR images by adaptively estimating complex covariance matrix-based multitemporal filtering, named CCM-MTF. The key idea is to employ generalized likelihood ratio (GLR) test on the Wishart distributed initial CCM to evaluate the similarity between two pixels. A special design is given to the initial CCM estimation, in which temporal samples are used instead of spatially neighboring samples. Then, a threshold determined by the asymptotic distribution of the logarithm of GLR test statistics at a fixed significance level is used to select spatial SHPs for the reference pixel. Subsequently, the filtering is implemented by estimation of the final CCM from original SAR scattering vector over SHP pixels, and all filtered target information channels including intensity, interferometric phase, and coherence can be explicitly derived from the final CCM. The effectiveness of the proposed CCM-MTF method is validated by experiments on both simulated and real multitemporal SAR images. Both qualitative and quantitative comparisons between CCM-MTF and four state-of-the-art SAR filters are carried out to demonstrate its advantages in terms of speckle suppression as well as detail preservation for all the three information channels.

Multiple InSAR techniques are increasingly being developed for earth observation. However, among them, persistent scatterers-based InSAR (PSI) techniques fail to obtain enough measurement points (MPs) in rural mountainous area due to the lack of persistent scatterers (PSs), such as ma-made structures, rocks, and outcrops, etc. In this paper, Distributed Scatterers-based InSAR (DS-InSAR) algorithm, exploiting both persistent scatterers and distributed scatterers (DSs) widely spreading in rural areas, is proposed to make up the limitation of persistent scatterers-based technique when monitoring mountainous landslides. There are two key steps to preprocess DSs in DS-InSAR algorithm: selecting DS candidates and estimating optimal interferometric phases. The selected DSs and PSs are combined for further processing using traditional PSI procedure. A qualitative and quantitative simulation analysis was operated to validate the DS-InSAR algorithm. Then, both PSI and DS-InSAR were implemented to monitor Xishan landslide in western mountainous region of Sichuan province, based on high-resolution TerraSAR-X images. The obtained results demonstrate that DS-InSAR could detect much more MPs and provide more reliable deformation information.

Terrain measurement and surface motion estimation are key applications for SAR missions. These applications drive several SAR satellite missions in the Dragon partner countries in Europe and China. In this context, we present our work in the Dragon program on terrain measurements with results from several test sites in China.

In this paper, a new approach developed to retrieve temporally-differenced maps of the spatial distribution of water vapor from interferometric synthetic aperture radar (InSAR) data with a horizontal resolution as fine as 20 m is described. To get a more accurate map of wet delay, we used the ERA-Interim to predict the dry delay component in the total atmospheric delay from InSAR. In addition, the ERA-Interim was used to compute the conversion factors required to convert the wet delay to precipitable water vapor (PWV). The InSAR-derived differential PWV maps were calibrated by means of the ground-based GPS PWV measurements. The approach was applied to map the temporal change of PWV over the Southern California, USA. We validated our results against measurements of the PWV acquired from a Medium-Resolution Imaging Spectrometer (MERIS) onboard the ENVISAT satellite. The results show strong spatial correlation with values of uncertainty of less than 2 mm.

The radiometric quality of polarimetric SAR (PolSAR)/SAR images is affected by terrain undulations, and the resultant radiometric distortions should be calibrated to facilitate quantitative applications as land cover classification. This paper presents a terrain-related radiometric calibration method to a quad-polarimetric Advanced Land Observing Satellite phased array type L-band synthetic aperture radar (ALOS PALSAR) image. A digital elevation model (DEM) was used for accurate detection of layover and shadow areas. Precise calibration was done subsequently. Polarimetric features were extracted and a supervised random forest (RF) classifier was then employed. Five classes were extracted as waterbody, bare soil, farmland, forest, and man-made objects. Accuracy assessment was performed and the results were analyzed. Improvement of overall accuracy from 78.67% to 82.67% and that of kappa coefficient from 0.73 to 0.78 was achieved using the radiometric-terrain-calibrated (RTC) features, which shows great necessity of RTC processing for PolSAR land cover classification in mountainous areas.

As an advanced technique, synthetic aperture radar (SAR) tomography makes it possible to overcome the layover problem induced by the intrinsic side-looking geometry of SAR sensors. However, traditional nonparametric spectral estimators, e.g., truncated singular value decomposition, are limited by their poor elevation resolution. On the other hand, the compressive-sensing-based approaches using the basis-pursuit strategy to find an L1-norm minimization solution for SAR tomography (TomoSAR) are extremely time consuming. Therefore, a fast and robust tomographic algorithm with super-resolution capability is needed. In this letter, we propose a new approach for TomoSAR based on two-step iterative shrinkage/thresholding (TWIST). TWIST uses a two-step strategy to speed up the L1-norm minimization procedure and can achieve an exceptionally fast convergence speed for TomoSAR. Experimental studies with simulated signals and a spotlight-mode TerraSAR-X data set were carried out to demonstrate the merits of the proposed TWIST approach in terms of robustness, fast convergence speed, and super-resolution capability.

SAR interferometry has long been regarded as an effective tool for wide-area topographic mapping in hilly and mountainous areas. However, quality of InSAR DEM product is usually affected by atmospheric disturbances and decorrelation-induced voids, especially for data acquired in repeat-pass mode. In this paper, we proposed an approach for improved topographic mapping by optimal fusion of multi-orbit InSAR DEMs with correction of atmospheric phase screen (APS). An experimental study with highresolution TerraSAR-X and COSMO-SkyMed datasets covering a mountainous area was carried out to demonstrate the effectiveness of the proposed approach. Validation with a reference DEM of scale 1:50,000 indicated that vertical accuracy of the fused DEM can be better than 5 m.

Energy is the major limiting factor for high-performance computing. Nowadays, modern Graphics Processing Units (GPU) use less power per floating-point operation. With the CUDA on ARM architecture, a low-power high performance architecture is available and we present first results on using this architecture for fast SAR geocoding and demonstrate the use of fast SAR geo-coding on GPU for a flexible SAR stereo-radargrammetry approach. We demonstrate the speed-up of SAR geo-coding with GPU implementations on an NVIDIA Tesla C2070 and the CARMA DevKit.

Persistent Scatterer Interferometry (PS-InSAR) is applied to derive displacement information with millimetric precision. Analyzing stable persistent scatterers from a large stack of SAR images,helps to overcome the geometrical and temporal decorrelation, which occur when using differential interferometry. The removal of the topographic phase with an external DEM seems to cause problems. In our experiment, we select three different DEMs: ASTER GDEM, a DEM derived from a digitized topographic map, and SRTM-3 in order to analyze the influence of the input DEMs for PS-InSAR processing in the Three Gorges area. We find that differential interferogram generation is related to the topographic influence for the PS-InSAR processing and different DEMs get us different PS-InSAR results.

The RPC (Rational Polynomial Coefficient) model can be used as a replacement sensor model for geo-coding spaceborne SAR data. A hybrid method, combining the L-curve and the IMCCV (Iteration method by correcting characteristic value) method, for solving ill-conditioned equations in the RPC model is proposed. The hybrid method can get higher accuracy at low cost of calculation time. Based on an example in Malaysia, the application for fast RPC geocoding for stereo radargrammetry is shown.