This paper introduces a novel approach for sequential estimation of the interferometric phase in the context of long Synthetic Aperture Radar (SAR) image time series. When newly acquired data arrive, the data set expands and can be partitioned into two distinct blocks. One represents the previous SAR images and the other represents the newly acquired data. The proposed approach (S-MLE-PL) exploits sequential maximum likelihood estimation of the covariance matrix of the whole data set, taking the existing data set as prior information. This approach facilitates the continuous interferometric phase estimation by incorporating the new data into the previous context. In addition, it presents the advantage of reduced computation time compared to the traditional approaches, making it a more efficient solution for operational displacement estimation.