Nowadays, data fusion constitutes the key subject in numerous applications of remotely sensed displacement measurements, with the increasing availability of remote sensing data and the requirement of improving the measurement accuracy. This article addresses the current status and challenges in the fusion of remotely sensed displacement measurements. An overview is given to discuss the remote sensing sources and techniques extensively used for displacement measurement and the recent development and achievement of displacement measurements fusion. The fusion between displacement measurements and the integration of a geophysical model are discussed. The fusion strategies and uncertainty propagation approaches are illustrated in two main applications: 1) the surface displacement measurements fusion to retrieve surface displacement with a reduced uncertainty in case of redundancy, with larger spatial extension, or of a higher level in case of complementarity, and 2) the surface displacement measurements fusion to estimate the geometrical parameters of a physical deformation model in case of redundancy and complementarity. Finally, the current status and challenges of remotely sensed displacement measurements fusion are highlighted. Moreover, some potential ways to deal with heterogeneous data types and to assimilate remote sensing data into physical models to realize near-real-time displacement monitoring are proposed.