格陵兰冰盖流速监测对定量估算冰盖物质损失以及研究冰盖对全球变暖的响应具有重要意义。利用SAR影像强度信息进行偏移量追踪是目前冰川流速监测的主要方法。冰川表面散射特性的变化会导致SAR影像强度信息发生改变，导致影像匹配失相关，从而造成提取的流速场中存在大量错误与空洞。为了克服该问题，本文提出了一套基于Sentinel-1 SAR影像提取冰川流速时序的数据处理流程：通过开运算、连通性分析、自适应中值滤波等方法去除单对追踪影像中的噪声与错误；同时利用现有产品的年度和月度平均流速数据完成基准校正并引入角度信息进一步去除部分噪声与粗差；最后通过间隔6、12、18日的追踪影像引入冗余配对，使用迭代的奇异值分解（SVD）方法求解时序方程组，构建冰川流速时序。将利用本方法提取的2018至2020年间格陵兰Petermann溢出冰川6日间隔冰流速时序与现有流速产品进行对比表明，与由单轨数据生成的CPOM冰川流速产品相比，本方法获得的流速时序噪声更少，流速场在时空上更连续平滑，在相同冰川范围内有效数据覆盖范围更广。与由多轨数据合成的PROMICE产品比较表明两者的精度和有效数据覆盖率类似，但本文方法提取的流速时序分辨率更高，有效数据覆盖率更加稳定，且在对于追踪效果较差的夏秋季本方法在抑制噪声方面表现更好。因此本文提出的算法能有效修补影像匹配空洞及剔除异常匹配，并合成高时空分辨率冰川流速时序，对利用星载SAR影像提取格陵兰冰盖流速监测具有重要意义。

Monitoring Greenland glacier flow velocity is essential for quantitative estimation of ice sheet material loss, assessment of the impact of global climate change on ice sheet dynamic, and evaluation of Greenland’s contribution to current sea-level rises. Offset-tracking technique is the main method for deriving glacier velocity by using the intensity information of SAR or optical images. Intensity offset tracking is less sensitive to decorrelation than the InSAR method and can be applied to images with long temporal intervals. However, glacier avalanche, ice avalanche, snowfall and melting-freezing cycles on glaciers still cause changes in the scattering characteristics of the surface, resulting in changes of the SAR image intensity, leading to a loss of correlation in matching between images, especially in summer. Object at providing more accurate glacier flow velocity field, this research proposes a novel data processing strategy of processing Sentinel-1 SAR data and takes the famous Petermann outlet glacier in Greenland as an example to extract its glacier velocity based on image tracking. Noise and errors in tracking images formed by single pairs of Sentinel-1 images are removed through morphological opening operation, connectivity analysis, adaptive median filtering, etc. Meanwhile, the annual and monthly Greenland ice flow velocity products are employed to select datum by taking its low-speed area as reference. We also introduce flow direction of the annual or seasonal glacier flow to filter out wrong matchings. Similar to the small-baseline analysis of the InSAR technique, redundant observation of tracking pairs with 6-, 12-, and 18-days intervals are then applied to the singular value decomposition (SVD) method to solve the time-series of glacier velocity, also to void the possible rank deficit. SVD is iteratively performed to remove the observed coarse error that could not be eliminated in the previous processing by checking residuals of the observation after each iteration. We obtain the time series glacier velocity for Petermann Glacier from 2018 to 2020 with temporal resolution of 6-day. Compared to the published glacier velocity products it finds that our derived results are less noisy, more continuous, smoother, and cover more area than the CPOM product which employs the same data source as we do. Compared to the PROMICE product produced from multi-track SAR data shows that we share similar accuracy and effective data coverage, but the results of this research have higher resolution and are less noisy, especially in summer. We conclude that the proposed algorithm can effectively eliminate the anomalous matching of single offset-tracking pair for forming high spatial and temporal resolution glacier flow velocity time series with redundant matching pairs by an iterative SVD method, which are essential for monitoring glacier flow velocity for Greenland Ice Sheet with satellite SAR images.