The LuTan-1 (LT-1) satellite, launched in 2022, is China’s first L-band full-polarimetric Synthetic Aperture Radar (SAR) constellation, boasting interferometry capabilities. However, given its limited use in subsidence monitoring to date, a comprehensive evaluation of LT-1’s interferometric quality and capabilities is necessary. In this study, we utilized the Differential Interferometric Synthetic Aperture Radar (DInSAR) technique to analyze mining-induced subsidence results near Shenmu City (China) with LT-1 data, revealing nine subsidence areas with a maximum subsidence of −19.6 mm within 32 days. Furthermore, a comparative analysis between LT-1 and Sentinel-1 data was conducted focusing on the aspects of subsidence results, interferometric phase, scattering intensity, and interferometric coherence. Notably, LT-1 detected some subsidence areas larger than those identified by Sentinel-1, attributed to LT-1’s high resolution, which significantly enhances the detectability of deformation gradients. Additionally, the coherence of LT-1 data exceeded that of Sentinel-1 due to LT-1’s L-band long wavelength compared to Sentinel-1’s C-band. This higher coherence facilitated more accurate capturing of differential interferometric phases, particularly in areas with large-gradient subsidence. Moreover, the quality of LT-1’s monitoring results surpassed that of Sentinel-1 in root mean square error (RMSE), standard deviation (SD), and signal-to-noise ratio (SNR). In conclusion, these findings provide valuable insights for future subsidence-monitoring tasks utilizing LT-1 data. Ultimately, the systematic differences between LT-1 and Sentinel-1 satellites confirm that LT-1 is well-suited for detailed and accurate subsidence monitoring in complex environments.

Due to the complex terrain and intense tectonic activity, and harsh climate in the Qinling-Daba Mountains, many landslides occur in the area. Most of these landslides are extremely active, posing a serious threat to the safety and property of local residents. As a mature deformation-monitoring technology, InSAR has been widely used in landslide detection, but the steep terrain and dense vegetation in the Qinling-Daba Mountains make detection challenging. Hence, it is important to choose suitable data sources and methods for landslide detection via InSAR in this area. This study was the first to collect ALOS/PALSAR−2 and Sentinel−1A images to detect landslides in the Qinling-Daba Mountains, applying a method combining IPTA and SBAS. In total, 88 landslides were detected and validated. The results show that the deformation-detection error rate of Sentinel−1A is 2% higher than that of ALOS/PALSAR−2 and that its landslide-recognition rate is 47.7% lower than that of ALOS/PALSAR−2. Upon comparing and analyzing the visibility, coherence, closed−loop residuals, and typical time series of landslide deformation from the two kinds of data, it was found that the extremely low quality of available Sentinel−1 A summer data is a major factor influencing that system’s performance. ALOS/PALSAR−2 is more likely to detect landslides in areas with high vegetation coverage, meeting more than 90% of the monitoring needs. It is thus highly suitable for landslide detection in the Qinling–Daba Mountains, where seasonality is significant. In this paper, for the first time, multiple data sources are compared in detail with regard to their utility in landslide detection in the Qinling–Daba Mountains. A large number of accuracy metrics are applied, and the results are analyzed. The study provides important scientific support for the selection of data sources for future landslide monitoring in the Qinling–Daba Mountain area and similar areas and for the selection of methods to evaluate the accuracy of InSAR monitoring.