Data　loss　is　a　common　phenomenon　in　the　practical　wireless　sensor　networks　due　to　several　observations,　such　as　the　node　or　link　failure,　the　expensive　cost　of　re-transmission,　and　so　on.　How　to　reconstruct　the　lost　data　is　one　important　issue　for　many　applications　which　concern　heavily　on　the　data　gathering　issues.　Most　of　the　current　data　reconstruction　solutions　are　based　on　the　spatial　correlation　and　they　adopt　interpolation　methods　to　approximate　the　data　reconstruction　process,　which　leads　to　bad　performance　especially　in　case　that　the　number　of　data　loss　increases　in　the　network.　In　this　paper,　we　consider　the　spatial　and　temporal　correlation　simultaneously,　and　propose　a　novel　data　reconstruction　algorithm　to　improve　the　data　error　ratio　in　the　wireless　sensor　networks.　The　proposed　algorithm　can　utilize　the　spatial　correlation　by　using　the　curved　face　reconstruction,　and　it　is　also　helpful　to　improve　the　accuracy　of　data　reconstruction　by　exploring　the　temporal　correlation　among　the　sensory　data.　Extensive　experiments　demonstrate　that　the　proposed　data　reconstruction　algorithm　can　significantly　reduce　the　fitting　data　error　ratio　compared　with　related　works　especially　in　case　that　data　loss　is　serious　in　the　network.　©　2016　ACM.