## 6 Conclusions  
In this paper, deformation was extracted using two data sources, Sentinel\(-\)1A and ALOS/PALSAR\(-\)2, based on the combination of IPTA and SBAS technology. Landslides were further interpreted by identifying deformation and integrating optical and geological factors, and 88 active landslides were observed in the region. Based on the hotspot analysis, there are many discrete error points in the Sentinel\(-\)1A rate results (2% more than in the results from ALOS/PALSAR\(-\)2), and there are also many missed detections in the concentrated deformation area. The deformation-rate consistency is poor, and the final landslide recognition rate of Sentinel\(-\)1A is also 47.7% lower than that of ALOS/PALSAR\(-\)2. A comparative analysis of the visibility, coherence, closed-loop residuals, and time series of individual landslides from the two data types shows that the main reason for their differences is that the quality of Sentinel\(-\)1A's summer data is extremely low, resulting in missed detection of many landslides. ALOS/PALSAR\(-\)2 is less affected by surface scatterers and time decoherence and is more likely to detect landslides in summer. This advantage makes it highly suitable for landslide detection in the Qinling-Daba Mountains, where seasonality is a significant factor, and this system can meet more than 90% of the monitoring needs. The time consistency of the two data sources is guaranteed to the greatest extent when the original data are selected, which leads to a smaller amount of Sentinel\(-\)1A data being included in the experiment. However, if this factor is not considered in the detection process, Sentinel\(-\)1A has the advantages of sufficient free data sources, which prolong its monitoring time. It could thus obtain better results than those seen in this experiment. However, for those areas with sudden deformation in summer and continuous oscillation of deformation trends, serious loss of coherence in dense vegetation and excessive deformation ranges mean that detecting landslides using Sentinel\(-\)1A data remains a difficult task. Therefore, when large-scale landslide detection is carried out, the time coverage of Sentinel\(-\)1A data should be extended as far as possible, including at least the data from before and after summer (best if these data are available for more than one year). For the key areas with high vegetation coverage and obvious seasonality, it is necessary to use ALOS/PALSAR\(-\)2 data for monitoring, and the amount of ALOS/PALSAR\(-\)2 data should be greater than 15 scenes to obtain the best effect (so as to better offset the impact of atmospheric errors). In this paper, a large number of accuracy indicators are used to evaluate the detection capabilities of Sentinel\(-\)1A and ALOS/PALSAR\(-\)2 in the Qinling-Daba Mountains. These systems are compared in detail for the first time, and the drivers of differences in these systems' data are also discussed. A more intuitive analysis verifies the feasibility of using two data sources for landslide detection and the necessity of using multi-source data detection in densely vegetation-covered areas. It is believed that this study can provide good suggestions for the selection of data sources for landslide detection in the Qinling-Daba Mountains and similar complex, dangerous mountainous areas with high vegetation coverage and large numbers of shallow landslides. The study also provides important scientific support for the selection of InSAR monitoring accuracy-evaluation methods. Although the experiment was as rigorous as possible, there are still some shortcomings in the data processing, such as inconsistent parameters, the use of a single data-processing method, and the small coverage. For future research, more data-processing methods, such as Stack\(-\)InSAR and PS\(-\)InSAR technology, should be used to fully verify the applicability of the data. More data sources should also be mined and compared.