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The pre-failure evolution of landslides is of great value for the analysis of landslide triggering factors and post-disaster stability assessment. At present, optical images are commonly employed to ana⁃ lyze the pre-failure evolution, but it is well-known that their data availability could be highly limited due to the presence of clouds. With the advance in radar remote sensing and interferometric synthetic aperture radar(InSAR), it could provide a new technical approach for landslide pre-failure detection. In this paper, the 2018 Nanyu landslide in Gansu Province is utilized to demonstrate the capability of InSAR to trace its pre-failure surface displacements using European Space Agency’s Sentinel-1 radar data with a temporal interval of 12 days in different tracks. The results show that the landslide began to deform in June of 2017, and the maximum cumulative deformation reached up to 77 mm in the 13 months before the occurrence of the landslide. The time series InSAR derive displacements and the rainfall data is consistent, suggesting that the rainfall should be one of the triggering factors for the landslide. The study demonstrates the potential of radar interferometry for landslide detection, which can provide insights on landslide triggering factors, landslide disaster prevention and mitigation, and even landslide monitoring and early warning work in the future. [ABSTRACT FROM AUTHOR]

Satellite radar observations enable us not only to detect landslides with detailed sliding signals over broad spatial extents, but also to track landslide dynamics continuously，which has gradually been recognized by the earth observation and landslide communities. However, there are still several challenges in the landslide detection and monitoring with satellite radar observations due to their inherent limitations such as the phase decorrelation caused by heavy vegetation and/or large gradient surface movements，and the geometric distortion introduced by the side-looking orbit. In this paper，from landslide detection and monitoring perspective，the four major challenges of satellite radar technologies are discussed：① The phase decorrelation caused by heavy vegetation can be weakened by use of synthetic aperture radar (SAR) imagery with a long radar wavelength (e. g. S-band or L-band)，a short temporal resolution，and/or a high spatial resolution (e. g. 1 m or even higher)，and/or advanced interferometric SAR (InSAR) time series，and the phase decorrelation associated with large deformation gradients can be addressed by SAR offset tracking and range splitspectrum interferometry techniques.② Atmospheric effects represent a big challenge of conventional InSAR for landslide detection and monitoring, especially in mountain areas. The generic atmospheric correction online service (GACOS) which is developed at Newcastle University can be used to reduce atmospheric effects on radar observations and simplify the follow-on time series analysis.③ The geometric distortions such as shadows and layovers can be pre-analyzed using an external digital elevation model (DEM) for medium-spatial-resolution SAR data； in contrast，for high-resolution SAR data，a machine learning approach can be used to identify water bodies，shadow and layover areas without a requirement of a high-spatial-resolution DEM.④ Residual topographic phase exhibits in areas with high buildings or steep slopes，which could easily lead to phase unwrapping errors; this can be tackled by a baseline linear combination approach. In addition, a framework is proposed to combine satellite radar technologies with other earth observations (e. g. ground-based radar, LiDAR and GNSS) to develop an automated landslide detection and monitoring system. It is expected that this paper will help the earth observation and landslide communities clarify the technical pros and cons of the satellite radar technologies so as to promote them and guide their future development. [ABSTRACT FROM AUTHOR]