Time series of ground subsidence can not only be used to describe motion produced by various anthropocentric and natural process but also to better understand the processes and mechanisms of geohazards and to formulate effective protective measures. For high-accuracy measurement of small baseline subset interferometric synthetic aperture radar (SBAS-InSAR), atmospheric turbulence and decorrelation noise are regarded as random variables and cannot be accurately estimated by a deterministic model when large spatio-temporal variability presents itself. Various weighting methods have been proposed and improved continuously to reduce the effects of these two parts and provide uncertainty information of the estimated parameters, simultaneously. Network-based variance-covariance estimation (NVCE) and graph theory (GT) are the two main weighting methods which were developed on the basis of previous algorithms. However, the NVCE weighting method only focuses on the influence of atmospheric turbulence and neglects the decorrelation noise. The GT method weights each interferogram in a time series by using the Laplace transformation. Although simple to implement, it is not reasonable to have an equal weight for each pixel in the same interferogram. To avoid these limitations, this study presents a new weighting method by considering the physical characteristics of atmospheric turbulence and decorrelation noise in SBAS-InSAR images. The effectiveness of the proposed method was tested and validated by using a set of simulated experiments and a case study on a Hawaiian island. According to the GPS-derived displacements, the average RMSE of the results from the new weighting method was 1.66 cm, indicating about an 8% improvement compared with 1.79, 1.80 and 1.80 cm from the unweighted method, the NVCE method and the GT method, respectively.