The accuracy of the velocity fifield will be affected by the noise model and common mode errors through GPS time series analysis. In order to analyze the inflfluence of these two factors on the accuracy of the velocity fifield, two kinds of data are used, including the three-year observation from 20 permanent GPS stations with high spatial correlation in the Everest, which is about 650 km from north to south and 1068 km from east to west, and three-year 80 ascending images and 141 descending images from sentinel-1A, which are processed by GAMIT/GLOBK software and Small Baseline Subset-Interferometric Synthetic Aperture Radar method (SBAS-InSAR), respectively. The vertical deformation rate is solved by time series analysis through a self-made adaptive algorithm. In the analysis, the linear change rate, period, half period coeffificient, and residual sequence of all stations are solved by using James L. Davis periodic model. The noise type of residual sequence is analyzed by the power spectrum model. The spatio-temporal correlated noise, Common Mode Error (CME), is extracted by the Principal Component Analysis (PCA) and Karhunen-Loeve (KLE) methods. The results show that noises can be best described by “flflicker noise þ white noise” model. After the removal of CME, the R2 estimates of all stations are above 0.8, with RMS value of velocity fifield decreasing from 1.428 mm/yr to 1.062 mm/yr and 1.063 mm/yr to 0.815 mm/yr, in N and E directions, respectively, indicating that the inflfluence of CME can't be ignored in the extraction of the high-precision velocity fifield in the Nepal and Everest region.