作为先进雷达遥感技术，时序合成孔径雷达干涉测量（Interferometric Synthetic Aperture Radar，InSAR）可高精度测量微小地表形变，在地学参数反演、基础设施监测等领域获得广泛应用。非零闭合相位（Nonzero Closure Phase，NCP），亦称相位偏差，打破了InSAR数据处理中相位一致性假设，导致常规小基线集（Small Baseline Subset，SBAS）InSAR方法的形变结果中出现系统性偏差，研究NCP特征及其去除方法是近年来雷达干涉测量领域的研究热点问题之一。首先，本文从数学物理方面系统性地阐述非零闭合相位产生机制和来源，进一步明确InSAR潜在误差源；然后在已有研究基础上，分析相位偏差与不同多视比下地物类型关系；最后，创新探究NCP在不同时间基线组合和干涉对数量两方面对常规SBAS形变测量结果的影响规律。研究表明：不同地物相位偏差表现不一致，植被覆盖类地物易受相位偏差影响，建成物地物受影响较小；除特殊多视比情况，不同多视比相位偏差无显著差异；不同时间基线组合情况下形变结果存在明显差异，短时间基线组合的形变偏差比长时间基线组合更大，引入长时间基线干涉对可有效缓解相位偏差影响，但增加短时间基线干涉对数量无显著效果，总体上，增加平均时间基线可进一步缓解相位偏差直至效果稳定。本文研究为常规SBAS方法的基线选择和相位偏差改正提供了详细的技术参考。

Time-series InSAR (Interferometric Synthetic Aperture Radar), as an advanced radar remote sensing technique, achieves high-precision measurements of subtle surface deformations, widely applied in fields of geological parameter inversion and infrastructure monitoring. The recent emergence of NCP (Nonzero Closure Phase), also referred to as phase bias, challenges the conventional assumption of InSAR phase consistencies. NCP introduces systematic biases into deformation results, particularly in SBAS-InSAR (Small Baseline Subset InSAR) method. Studying the characteristics of NCP and exploring methods for its removal are currently among the hot issues in the fields of InSAR. The study first explains the mathematical and physical mechanisms of NCP generation and analyzing the sources of phase bias. Then, based on existing research, we explore the relationship between phase bias and land cover types and multi-look ratio. Finally, innovative investigate the impact patterns of NCP on conventional SBAS deformation measurements concerning different time baseline combinations and interferometric pair quantities. The results shows that different land cover types show varied phase bias, with vegetation-covered surfaces being more affected, while built-up areas experience less impact. no significant bias difference with different multi-look ratio excluding 1:1. Different time baseline combinations yield distinct deformation results, with shorter baselines combination being more susceptible to bias than longer ones. Introducing long time baseline effectively mitigates phase bias. However, increasing short time baseline does not significantly mitigate phase bias. Increasing the average time baseline further mitigate bias until stability. This study provides valuable insights for baseline selection and phase bias correction in conventional SBAS methods.