Landslide identification and monitoring are two significant research aspects for landslide analysis. In addition, landslide mode deduction is key for the prevention of landslide hazards. Surface deformation results with different scales can serve for different landslide analysis. L-band synthetic aperture radar (SAR) data calculated with Interferometric Point Target Analysis (IPTA) are first employed to detect potential landslides at the catchment-scale Wudongde reservoir area. Twenty-two active landslides are identified and mapped over more than 2500 square kilometers. Then, for one typical landslide, Jinpingzi landslide, its spatiotemporal deformation characteristics are analyzed with the small baseline subsets (SBAS) interferometric synthetic aperture radar (InSAR) technique. High-precision surface deformation results are obtained by comparing with in-situ georobot measurements. The spatial deformation pattern reveals the different stabilities among five different sections of Jinpingzi landslide. InSAR results for Section II of Jinpingzi landslide show that this active landslide is controlled by two boundaries and geological structure, and its different landslide deformation magnitudes at different sections on the surface companying with borehole deformation reveals the pull-type landslide mode. Correlation between time series landslide motion and monthly precipitation, soil moisture inverted from SAR intensity images and water level fluctuations suggests that heavy rainfall is the main trigger factor, and the maximum deformation of the landslide was highly consistent with the peak precipitation with a time lag of about 1 to 2 months, which gives us important guidelines to mitigate and prevent this kind of hazard.

On the afternoon of 28 June 2010, an enormous landslide occurred in the Gangwu region of Guanling County, Guizhou Province. In order to better understand the mechanism of the Guanling landslide, archived ALOS/PALSAR data was used to acquire the deformation prior to the landslide occurrence through stacking and time-series InSAR techniques. First, the deformation structure from InSAR was compared to the potential creep bodies identified using the optical remote sensing data. A strong consistency between the InSAR detected deformed regions and the creep bodies detected from optical remote sensing images was achieved. Around 10 creep bodies were suffering from deformation. In the source area, the maximum pre-slide mean deformation rate along the slope direction reached 160 mm/year, and the uncertainty of the deformation rates ranged from 15 to 34 mm/year. Then, the pre-slide deformation at the source area was analyzed in terms of the topography, geological structure, and historical rainfall records. Through observation and analysis, the deformation pattern of one creep body located within the source area can be segmented into three sections: a creeping section in the front, a locking section in the middle, and a cracking section in the rear. These sections constitute one of the common landslide modes seen in the south-west of China. This study concluded that a sudden shear failure in the locking segment of one creeping body located within the source area was caused by a strong rainstorm, which triggered the Guanling landslide.

The Interferometric Synthetic Aperture Radar (InSAR) technique is a well-developed remote sensing tool which has been widely used in the investigation of landslides. Average deformation rates are calculated by weighted averaging (stacking) of the interferograms to detect small-scale loess landslides. Heifangtai loess terrace, Gansu province China, is taken as a test area. Aiming to generate multi-temporal landslide inventory maps and to analyze the landslide evolution features from December 2006 to November 2017, a large number of Synthetic Aperture Radar (SAR) datasets acquired by L-band ascending ALOS/PALSAR, L-band ascending and descending ALOS/PALSAR-2, X-band ascending and descending TerraSAR-X and C-band descending Sentinel-1A/B images covering different evolution stages of Heifangtai terrace are fully exploited. Firstly, the surface deformation of Heifangtai terrace is calculated for independent SAR data using the InSAR technique. Subsequently, InSAR-derived deformation maps, SAR intensity images and a DEM gradient map are jointly used to detect potential loess landslides by setting the appropriate thresholds. More than 40 active loess landslides are identified and mapped. The accuracy of the landslide identification results is verified by comparison with published literatures, the results of geological field surveys and remote sensing images. Furthermore, the spatiotemporal evolution characteristics of the landslides during the last 11 years are revealed for the first time. Finally, strengths and limitations of different wavelength SAR data, and the effects of track direction, geometric distortions of SAR images and the differences in local incidence angle between two adjacent satellite tracks in terms of small-scale loess landslides identification, are analyzed and summarized, and some suggestions are given to guide the future identification of small-scale loess landslides with the InSAR technique.

Twenty‐eight ALOS/PALSAR‐1 and ALOS/PALSAR‐2 images acquired from January 2007 to August 2018 were exploited to characterize the deformation history and temporal evolution of the Baige landslide, China, which successively occurred on 11 October and 3 November 2018. To mitigate the errors caused by the topographic relief and to conduct offset estimates between the SAR images from different platforms, a novel offset‐tracking method is proposed. The SAR images were ortho‐rectified firstly. Then two‐dimensional deformations were retrieved successfully from SAR images acquired at an identical platform and different platforms. Our results indicate that the maximum cumulative deformation in the line‐of‐sight direction of the Baige landslide reached about −60 m between January 2007 and August 2018. Furthermore, correlation between time series deformation and monthly precipitation and soil moisture derived from SAR intensity images suggests that the failure of the Baige landslide was closely related to the heavy rainfall in the summer of 2018.

The Heifangtai loess terrace in northwest China is a well-known area to study loess landslides because of its frequent occurrence, various types, and complex trigger factors. The determination of loess landslide type and failure mode is of great significance for the landslide risk assessment, hazard mitigation, and prevention. In this study, ascending and descending Spot-mode TerraSAR-X datasets are employed to analyze the deformation patterns and failure modes of loess landslides in Xinyuan landslide group, Heifangtai terrace, by using multidimensional small baseline subsets (MSBAS) technique. First, the locations of three active landslides are delineated by independent InSAR observations from both ascending and descending TerraSAR-X datasets. Then, two-dimensional deformation rates and time series results in both vertical and horizontal east-west directions of the identified landslides are calculated using MSBAS technique. Finally, the deformation types and failure modes of landslides in the study sites are analyzed by jointly using the two-dimensional deformation rates and time series results, topographic map, remote sensing images, and previous studies on the loess landslide failure modes. With the aid of complementary data including topographic map, remote sensing image, previous studies on the loess landslide failure modes, and field investigations, two-dimensional deformation results derived from ascending and descending SAR images are compatible with three typical failure modes of loess landslide including loess-bedrock planar slide, retrogressive failure, and loess slide. Furthermore, the two-dimensional deformation derived from InSAR technique can give much detailed deformation characteristics and movement of loess landslide.