Sentinel-1A satellite data is characterized by its broad coverage, rapid revisit periods, and cost-effectiveness, allowing for the efficient acquisition of large-scale surface deformation information along pipelines through InSAR technology. However, with complex terrain, large topographic relief and lush vegetation, phenomena such as layover, shadow, and incoherence commonly arise during monitoring. The applicability of Sentinel-1A satellite data in different sections of the pipeline is different. To evaluate the applicability of Sentinel-1A data in surface deformation measurements, three pipeline sections in varying mountainous terrains were selected as study areas. Correlation analyses were conducted by combining Sentinel-1A data, Sentinel-2 data, and ALOS DEM data, in order to construct applicability evaluation indexes. The results show that the ratio of layover and shadow regions to the total areas in Sentinel-1A images along the pipelines is significantly negatively correlated with the slope, with Pearson correlation coefficient of -0.914 and Spearman correlation coefficient of -1. Likewise, there was a significant negative correlation between image coherence and normalized vegetation index, with Pearson correlation coefficient of -0.972 and Spearman correlation coefficient of -0.99. Applicability evaluation indexes for slope and vegetation were established for Sentinel-1A data along pipelines in mountainous areas using the method of regression analysis and normalization, which can evaluate the applicability of Sentinel-1A data for deformation monitoring along the mountainous pipelines.