A recent development in Interferometric Synthetic Aperture Radar (InSAR) technology is integrating multiple SAR satellite data to dynamically extract ground features. This paper addresses two relevant challenges: identification of common ground targets from different SAR datasets in space, and concatenation of time series when dealing with temporal dynamics. To address the first challenge, we describe the geolocation uncertainty of InSAR measurements as a three-dimensional error ellipsoid. The points, among InSAR measurements, which have error ellipsoids with a positive cross volume are identified as tie-point pairs representing common ground objects from multiple SAR datasets. The cross volumes are calculated using Monte Carlo methods and serve as weights to achieve the equivalent deformation time series. To address the second challenge, the deformation time series model for each tie-point pair is estimated using probabilistic methods, where potential deformation models are efficiently tested and evaluated. As an application, we integrated two Radarsat-2 datasets in Standard and Extra-Fine modes to map the subsidence of the west of the Netherlands between 2010 and 2017. We identified 18128 tie-point pairs, 5 intersection types of error ellipsoids, 5 deformation models, and constructed their long-term deformation time series. The detected maximum mean subsidence velocity in Line-Of-Sight direction is up to 15 $\mathrm{mm}{\mathrm{yr}}^{-1}$. We conclude that our method removes limitations that exist in single-viewing-geometry SAR when integrating multiple SAR data. In particular, the proposed time-series modeling method is useful to achieve a long-term deformation time series of multiple datasets.

干涉式合成孔径雷达（InSAR）技术的最新发展是集成多个SAR卫星数据以动态提取地面特征。本文解决了两个相关的挑战：从空间中的不同SAR数据集中识别共同的地面目标，以及在处理时间动态时串联时间序列。为了解决第一个挑战，我们将InSAR测量的地理位置不确定性描述为三维误差椭球。在InSAR测量中，具有误差椭圆体且正体积为正的点被标识为代表多个SAR数据集中常见地面对象的联系点对。交叉体积是使用蒙特卡洛方法计算的，并用作权重以实现等效变形时间序列。为了解决第二个挑战，使用概率方法估计每个联系点对的变形时间序列模型，并在其中有效测试和评估潜在的变形模型。作为应用程序，我们以标准和超精细模式集成了两个Radarsat-2数据集，以绘制2010年至2017年之间荷兰西部的沉降图。我们确定了18128个连接点对，5个误差椭球的相交类型，5个变形模型，并建立了它们的长期变形时间序列。在视线方向上检测到的最大平均沉降速度最高为15 我们以标准和超精细模式集成了两个Radarsat-2数据集，以绘制2010年至2017年荷兰西部的沉降图。我们确定了18128个连接点对，5个误差椭球的相交类型，5个变形模型，并构建了他们的长期变形时间序列。在视线方向上检测到的最大平均沉降速度最高为15 我们以标准和超精细模式集成了两个Radarsat-2数据集，以绘制2010年至2017年荷兰西部的沉降图。我们确定了18128个连接点对，5个误差椭球的相交类型，5个变形模型，并构建了他们的长期变形时间序列。在视线方向上检测到的最大平均沉降速度最高为15$\mathrm{毫米}{年}^{--\mathrm{1个}}$。我们得出的结论是，当集成多个SAR数据时，我们的方法消除了单视图几何SAR中存在的限制。特别地，所提出的时间序列建模方法对于获得多个数据集的长期变形时间序列是有用的。