The differential interferometric synthetic aperture radar (DInSAR) technique can estimate the Earth’s surface displacement theoretically at centimeter- or millimeter-level accuracy, where the 2-D phase unwrapping (PU) is ineluctable, so that improper PU algorithm would deteriorate the accuracy. Conventional single-baseline (SB) PU-based DInSAR cannot accurately monitor high-gradient deformation due to the limitation of the phase continuity assumption. The conventional multibaseline (MB) PU-based DInSAR, including multifrequency-based two-pass DInSAR and two-stage programming approach (TSPA)-based three-pass DInSAR, is capable of delineating high-gradient deformation but imposes stringent requirements on InSAR data. Under this condition, we present a multitemporal baseline PU approach (MTBA) for two-pass DInSAR from single radar system such that even high-gradient deformation can be delineated at centimeter- or millimeter-level accuracy. The MTBA demonstrates to be valid through a simulation experiment and three strike-slip earthquake events. Furthermore, the MTBA expands the perpendicular baseline-based MB PU for topography mapping into the temporal baseline-based MB PU domain for deformation monitoring, potentially enriching the MB PU theory and extending the application of DInSAR technique.

The hybrid along-track (AT)/cross-track (XT) synthetic aperture radar interferometry (InSAR) system enables the measurement of radial sea surface velocity, where solving it through phase unwrapping (PU) is intrinsically ill-posed due to the coupling of velocity-induced and height-induced phase components. Conventional hardware-based methods impose stringent and costly system requirements, while auxiliary-data-dependent approaches are error susceptible. Under this condition, we innovatively propose an along-track two-stage programming approach (AT-TSPA) multibaseline (MB) PU method within the three-pass differential InSAR (DInSAR) framework. AT-TSPA addresses the ill-posedness by making use of the normal baseline diversity, thus enabling accurate and robust retrieval of sea surface velocity without relying on hardware upgrades or external auxiliary data. Both theoretical analysis and experimental results demonstrate that AT-TSPA is effective and practical for accurately measuring sea surface velocity under complex oceanic conditions. AT-TSPA is also applicable to other fields involving coupled velocity and height phase components, such as sea ice velocity estimation. Furthermore, AT-TSPA extends the theory of TSPA-based MB InSAR from terrestrial to oceanic domains, thereby advancing the applicability and practicability of well-posed InSAR techniques.

Accuracy assessment of interferometric synthetic aperture radar (InSAR) products without external reference data (ERD) has been a long-standing challenge because traditional phase unwrapping (PU) is an ill-posed problem. The limitations in accuracy assessment make it impossible to verify the precision of the PU results under actual observation conditions, so that the reliability of InSAR products in practical applications is unknowable. However, multibaseline (MB) PU is well-posed, so its accuracy can be evaluated in a statistical sense without using ERD, i.e., even if there are no in situ data, the accuracy of the products generated by MB InSAR can still be assessed theoretically. In this article, by obtaining the closed form optimality condition of the Chinese remainder theorem (CRT) optimization model, the new independent quantitative index for MB PU accuracy evaluation was mathematically established. Interestingly, we found that: 1) the optimality condition is a sufficient and necessary condition for the accuracy assessment of the MB PU and 2) it is affected by the normal baseline lengths of the MB InSAR system and the interferogram noise intensity. To practically apply this mathematical condition, a deep convolutional neural network (DCNN) was developed to refine MB InSAR product accuracy. The validity and effectiveness of the proposed approach have been systematically verified using simulated and acquired interferometric datasets.

The elevation data obtained from radar altimetry can assist interferometric synthetic aperture radar (InSAR) technology in achieving more accurate absolute height calibration in generating digital elevation model (DEM). The traditional height calibration method determines the calibration constant by comparing the InSAR DEM with the reference DEM after InSAR generates the DEM. This paper proposes a calibration method during the phase signal processing that not only accomplishes height calibration but also helps InSAR break the phase continuity assumption using radar altimetry to obtain accurate absolute phase, thereby improving DEM product quality. Experiments conducted in the southern region of Greenland demonstrate that the integration of radar altimetry data facilitates the seamless combination of InSAR phase unwrapping and height calibration.

Although the multi-baseline (MB) phase unwrapping (PU) approach does not rely on the phase continuity assumption embedded in the single-baseline (SB) PU approach, it is vulnerable to noise. The two-stage programming approach (TSPA) improves the noise robustness by using the gradient information of the interferogram. However, the TSPA is primarily designed for topography reconstruction. Then, we incorporate the temporal baseline into the TSPA to detect severe deformation, developing the time-series (TS) TSPA (TS-TSPA). The TS-TSPA estimates the absolute phase gradient by combing different temporal baseline lengths and then utilizes the L1-norm optimization model to obtain PU results. Two differential interferograms of 14 April 2021-20 May 2021 and 20 May 2021-1 June 2021 after the Maduo earthquake were showcased. The earthquake had a moment magnitude of Mw 7.4, occurring on 22 May 2021. The quake caused severe surface deformation, deviating or invalidating the phase continuity assumption. With successful PU results by the TS-TSPA, a fault line was identified, and estimated surface displacements ranged from −4.5 to 3.7 m along the line-of-sight (LOS) direction. The PU results of the SB PU method missed the fault line and only detected displacements between 0 and 1.3 m. Therefore, the TS-TSPA effectively detects significant surface deformation where the quake-induced damage could be devastating and outperforms the SB PU method to unwrap phases in damaged areas and fault line delineations.

In the small baseline subset (SBAS) algorithm, the zero-value closure phase assumption facilitates two-dimensional single-baseline phase unwrapping (PU) and then derives the surface deformation time series. The premise can fail when significant decorrelation occurs. Thus, we propose an optimization-based PU method by integrating the minimum-cost flow (MCF) model with the minimized closure phases. Three differential interferograms in 2017, i.e., 26 May-07 June, 07 June-19 June, and 19 June-26 May, Mao County of China, were unwrapped with the proposed method. The closure phases ranged from −6 to 7 rad, with an average of 0.01 and one standard deviation of 0.77 rad. Image cells having closure phases within ±1 rad are about 71%. Comparatively, closure phases of the MCF method were ±18 rad, with a mean of 0.55 and one standard deviation of 3.45 rad. Only ~30% of phase values were within ±1 rad. Hence, the studied PU method effectively minimizes the closure phases, thus potentially improving the deformation-related phase unwrapping used in the SBAS algorithm.

In a multi-temporal (MT) interferometric synthetic aperture radar (InSAR) algorithm, if the Itoh condition is not met, the closure phase of zero or near zero is no longer true. To resolve it, we apply a phase gradient network (PGNet) to assess a cell's phase gradient not subjected to the Itoh condition. The PGNet has been trained over rugged terrain in Mao County (32.06°N, 103.65°E), Sichuan Province, China. Then, the network was applied to predict phase gradients of three differential interferograms, i.e., 18 July-30 July, 30 July-11 August, and 11 August-18 July of 2017, Jiuzhaigou, Sichuan. A Ms (surface-wave magnitude) 7.0 earthquake (33.20° N, 103.82° E) occurred on 8 August 2017 in Jiuzhaigou. The PGNet has 1,175,000 cells with 0 for the horizontal closure phase gradient and 1,149,000 for the vertical closure phase gradient. (n = 1,413,120) For the Itoh condition-based method, the cell numbers are 984,800 horizontally and 1,094,000 vertically, less than those after the PGNet. Thus, the PGNet is more effective in minimizing the closure phase than the Itoh condition, improving an MT-InSAR algorithm's performance.

A technique able to acquire the height, especially for an area with a large spatial extent, is of great interest for forest biomass estimation. In this study, an inversion algorithm for tree height using the InSAR technique and dual-pol ALOS/PALSAR datasets was studied. The algorithm used the interferometric phase difference, coupled with two height compensation factors. When applied in the tree height estimate at Duke Forest, North Carolina, USA, it performed satisfactorily. As verified by in situ height measurement, the algorithm should have the potential to be one possible approach to estimate tree heights at regional and global scales.

In the SBAS-InSAR process, the conventional approach of determining ground control points (GCPs) can be error-prone. Thus, a new GCP selection approach using the phase derivative variance is proposed. Analyzing 45 descending-orbit Sentinel-1 datasets between October 2014 and June 2017 over the 2017 Xinmo landslide site, we separately identified ~10,600 and 100 GCP candidates with the conventional and proposed approaches. With four GCPs from the conventional method, the time-series displacement curves do not capture the accelerated deformation related to the landslide event. Corresponding to two sets of GCPs from the proposed approach, the displacement curves change gradually and capture the accelerated deformation. Thus, the approach can efficiently identify a small number of reliable and accurate GCP candidates for the SBAS-InSAR analysis.

Under the interferometric wide swath (IW) mode, the Sentinel-1 synthetic aperture radar (SAR) incidence angles vary from 29.0° at the near range to 46.0° at the far range. When the side-looking SAR datasets are used in the rugged terrain, the observable areas are functions of the SAR incidence angle and the local slopes and aspects. Thus, the application of the data is influenced. In this study, the 2017 Xinmo landslide event is used to demonstrate the influence. Before the landslide event, the observable areas at the initial landslide zone varied from 200,000 m2at the incidence angle of 29.0° to 80,000 m2at the incidence angle of 46.0°. The reduction of the observable area should be significant. In the extreme case, the visible area might become too small such that the InSAR-derived result could not be reliable. Caution should be exercised. Finally, knowing the observable area should complement the InSAR analyses of the coherence, interferograms, surface deformation rate, and deformation through time.

Kangding Airport, Sichuan, China located on the eastern margin of the Qinghai-Tibet Plateau has been developed in rugged terrain. The airport environment is characterized by high filling of earth materials, high frequencies and intensities of seismic activities, and high altitude. The understanding of potential risk of the 3-high airport assessed by the surface deformation is of vital importance. In this study, the Small Baseline Subset (SBAS) and Quasi Persistent Scatterer (QPS) techniques were used and proven to be effective to quantify the long-term surface deformation of the airport. The deformation was highly correlated with the local geological settings and annual climate cycle.

Dam construction is one of the solutions to the problem in water resources distribution and utilization caused by global climate change and the calculation of water energy reserves can provide scientific decision for it. The emergence of remotely sensed data and the application of Geographic Information System (GIS) make water reserves calculation a reality on a large scale. Hydro30 and measured runoff data are adopted to extract indicators of water energy reserves and the analysis of its spatial distribution is carried out in Anabar basin, Russia. The result shows that the most suitable site for waterpower development is concentrated in the upper reaches and the reserves of main rivers are larger than that in the source and tributary rivers.

Xinmo landslide event, China occurred on 24 June 2017. The head of the landslide area was ~3,600m above the mean sea level. Then, along the high to low elevation direction, a stable zone (S1) above the head area, an unstable zone (that is divided into an upper unstable subsidence area, UU; and a lower unstable uplift area, LU), and a stable area (S2) before the landslide event were identified. The time-series surface deformation was studied using the SBAS-InSAR technique and Sentinel-1 multi-temporal datasets acquired between November of 2015 and June of 2017. The mean deformation value was -8.3mm in S1, -26.9mm in UU, 27.9mm in LU, and - 1.6mm in S2, respectively. Zoning characteristics of moving earth materials in a typical landslide event were observed. After removing the deformation values in two stable zones, the difference in absolute value of the subsidence and uplift in the unstable zone was about 80mm, 100mm, and 130mm on 24 February 2017, 19 May 2017, and 24 June 2017, respectively. No landslide event occurred in February and May. Thus, with the preliminary findings, a warning might be issued once the absolute value was greater than 100mm.

Small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) technique was used to analyze the surface deformation from 2014 to 2017 at Heifangtai, Yongjing County, Gansu Province, China. The significant subsidence areas were delineated. Five potential landslide sites near the edge of Heifangtai were identified. In comparison of the surface deformation patterns among five sites, site A had the largest deformation value. The deformation might be accelerated after June of 2017. On 1 October 2017, a landslide event happened at site A. Thus, factors closely linked to the event was the magnitude and rate of surface deformation. With the findings coupled with other investigations, one should be able to identify potential landslides using the SBAS-InSAR technique and multi-temporal SAR datasets.