This chapter considers the change detection problem in a time series of polarimetric synthetic aperture radar (SAR) images using the covariance representation of multilook polarimetric SAR data. The change detection pipeline consists of an omnibus test for testing equality over the whole time span and a subsequent factorization used in assessing individual change time points. SAR data are very useful for many applications in remote sensing due to SAR's all-weather capabilities. The global test for forest concludes that there are no changes in the sequence of data. The reduction of backscatter from the flooded areas in both the VV and VH bands corresponds to a negative definite covariance matrix difference, the rapid receding of flood waters and hence increased backscatter due to the appearance of the original terrain to a positive definite change. Software for sequential SAR change detection is available in MATLAB and on GitHub in Python.