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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.