On 17 June 2020, an ancient landslide was partially reactivated close to the Aniangzhai village of Danba County in Sichuan Province of Southwest China. It was initiated by the erosion of the slope toe from the overflow of a dammed lake that was created due to heavy rainfall and the resulting debris flows coming from Meilonggou Gully to the Xiaojinchuan River. In this study, we report investigations on precursory and post-failure slope stability analysis exploiting optical and radar satellite remote sensing data. Using sub-pixel cross-correlation of optical data from Planet and Sentinel-2, we first derive the direction and magnitude of the main landslide failure. Advanced multi-temporal InSAR (MT-InSAR) analysis using Sentinel-1 and TerraSAR-X SAR data are then exploited to investigate the landslide kinematics before and after the big failure. Moreover, we report our experience on using a newly designed artificial corner reflector (CR), which is a half-round dihedral corner reflector (hr-DCR), for monitoring slope inability in this region using both ascending and descending SAR data. The CRs are quite useful auxiliaries for InSAR analysis as they could be recognized as stable targets during radar acquisitions, especially in the vegetated, semi-vegetated, or agricultural areas, where the widespread loss of coherence between consecutive image acquisitions could happen. Using MT-InSAR analysis, we observe precursory deformation amounting to approximately 50 mm/year in the year 2018-2020, reaching to a maximum of 270 mm/year for the post-failure period from Nov 2020 to June 2021. Before the main landslide failure in June 2020, the average deformation rate was approximately 14% higher in 2018-2020, dominated by above-average precipitation in summer, in comparison to the rate in 2014-2017. Interestingly, MTI analysis also detects a clear signal for the new instability and slow creep in the adjacent slope of the Aniangzhai ancient landslide, previously unrecognised in landslide inventory maps. Besides, the performance of newly designed DCRs is qualified and quantified in the experiments based on intensity time series (in dB), Signal-to-Cluster Ratio (SCR), and results from MTI time series.