 This paper proposes a novel approach to big NSAR data dynamical processing, BIDDP. The approach is designed to address the challenges associated with decorrelations and large gradient deformations in NSAR data. It utilizes a stepwise temporal phase optimization method, STPOM, to reduce decorrelation and a sequential estimation theory, set algorithm to estimate deformation parameters. Additionally, the paper introduces a quality index, QI, to measure the quality of deformation parameters. The proposed BIDDP approach was tested on a real dataset from the beige landslide in China. The results show that the proposed approach can effectively reduce decorrelation and improve the accuracy of deformation parameters. This article was authored by Baohang Wang, Chaoying Zhao, Qin Zhang, and others.