Modern　spatial　temporal　data　are　often　collected　from　sensor　networks.　Missing　data　problems　are　common　to　this　kind　of　data.　Making　accurate　imputation　is　important　for　many　applications.　In　the　unsupervised　setting,　one　technique　is　to　minimize　the　rank　of　a　tensor　or　matrix.　If　we　add　related　covariates,　can　we　get　more　accurate　imputation　results?　To　address　this,　we　transform　the　original　sensorxtime　measurements　to　high　order　tensors　by　adding　additional　temporal　dimensions　and　then　integrate　tensor　regression　with　tensor　completion　using　nuclear　norm　penalty.　One　advantage　is　we　can　simultaneously　estimate　parameters　and　impute　missing　values　due　to　clear　spatial　consistency　for　near-sited　spatial-temporal　data.　The　proposed　method　doesn＇t　assume　missing　mechanism　of　the　response.　Theoretical　properties　of　the　proposed　estimator　are　investigated.　Simulation　studies　and　real　data　analysis　are　conducted　to　verify　the　efficiency　of　the　estimation　procedure.