Spatiotemporal Filling of Missing Data in Remotely Sensed Displacement Measurement Time Series

Missing data is a critical pitfall in the investigation of remotely sensed displacement measurement because it prevents from a full understanding of the physical phenomenon under observation. In the sight of reconstructing incomplete displacement data, this letter presents a data-driven spatiotemporal gap-filling method, which is an extension of the expectation–maximization-empirical orthogonal fu...