The paper explores the potential of the satellite advanced differential synthetic aperture radar interferometry (A-DInSAR) technique for the identification of impending slope failure. The advantages and limitations of satellite InSAR in monitoring pre-failure landslide behaviour are addressed in five different case histories back-analysed using data acquired by different satellite missions: Montescaglioso landslide (2013, Italy), Scillato landslide (2015, Italy), Bingham Canyon Mine landslide (2013, UT, USA), Big Sur landslide (2017, CA, USA) and Xinmo landslide (2017, China). This paper aimed at providing a contribution to improve the knowledge within the subject area of landslide forecasting using monitoring data, in particular exploring the suitability of satellite InSAR for spatial and temporal prediction of large landslides. The study confirmed that satellite InSAR can be successful in the early detection of slopes prone to collapse; its limitations due to phase aliasing and low sampling frequency are also underlined. According to the results, we propose a novel landslide predictability classification discerning five different levels of predictability by satellite InSAR. Finally, the big step forward made for landslide forecasting applications since the beginning of the first SAR systems (ERS and Envisat) is shown, highlighting that future perspectives are encouraging thanks to the expected improvement of upcoming satellite missions that could highly increase the capability to monitor landslides’ pre-failure behaviour.

Ten small rock slides (with a volume ranging from 10¹ to 10³ m³) on a slope affected by working activities were detected, located, and timed using pictures collected by an automatic camera during 40 months of continuous monitoring with terrestrial SAR interferometry (TInSAR). These landslides were analyzed in detail by examining their pre-failure time series of displacement inferred from high-sampling frequency (approximately 5 min) TInSAR monitoring. In most of these cases, a typical creep behavior was observed with the displacement starting 370 to 12 h before the collapse. Additionally, an evident acceleration decrease of the displacement a few hours before the failure was observed in some rock/debris slides, thus suggesting the possibility of a mechanical feature of the slope that differs from the classical creep theory. The efficacy of the linear Fukuzono approach for the prediction of time of failure was tested by back-analyzing the ten landslides. Furthermore, a modified Fukuzono approach named average data Fukuzono (ADF) was implemented and applied to our dataset. Such an approach is able to improve forecasting effectiveness by reducing the error due to anomalies in the time series of displacement, like the acceleration decrease before failure. A prediction with a temporal accuracy of at least 2 h was obtained for all the analyzed rock/debris slides.