The accuracy of geophysical parameter estimation made with Interferometric Synthetic Aperture Radar (InSAR) time-series techniques can be improved with rapidly increasing available data volumes, and with the development of noise covariance matrices applicable to joint analysis of networks of interferograms. Here we present a physics-based decorrelation phase covariance model and discuss its role in noise reduction in unwrapped interferometric phase stacks. We demonstrate with an example wherein we average unwrapped interferogram phase stacks that span over a transient event how a noise covariance model can aid in noise reduction. Our model suggests that, for rapidly decorrelating surfaces (i.e., surfaces with much shorter correlation time than SAR acquisition intervals), it is preferable to incorporate all available interferograms from long observation windows. For slowly decorrelating surfaces (i.e., surfaces with longer correlation time than SAR acquisition intervals), our model suggests that a small subset of interferometric pairs is sufficient. We validate our model and three existing models of decorrelation phase covariance matrices in both Cascadia – a region with heavy vegetation cover, and Death Valley – a desert region, with C-band Sentinel-1 A observations. Our proposed model matches observations with the smallest average discrepancy between theory and observations.