This article introduces functional singular spectrum analysis (FSSA), an extension of singular spectrum analysis for functional time series. By defining a functional trajectory operator and developing a computationally tractable functional singular value decomposition (fSVD), FSSA decomposes functional time series into interpretable components. The method integrates ideas from functional data analysis and univariate SSA and is demonstrated using remote sensing data. An R package and interactive Shiny application are provided.