Sparse　mobile　crowdsensing　(MCS)　requires　participants　to　collect　data　from　partial　cells　and　then　intelligently　infer　the　data　of　the　rest　cells.　Since　collecting　data　from　different　cells　will　probably　result　in　different　data　inference　quality,　cell　selection　(i.e.,　which　cells　need　to　be　selected　to　collect　data)　is　a　critical　issue　in　Sparse　MCS.　Currently,　state-of-the-art　cell　selection　algorithms　are　implemented　based　on　reinforcement　learning.　These　algorithms　ignore　the　problem　that　the　urban　environment　is　usually　time　dependent,　and　the　cell　selection　model　needs　to　be　kept　up-to-date　to　adapt　to　the　time-dependent　environment.　However,　Sparse　MCS　applications　require　participants　to　collect　data　only　in　a　few　cells,　which　makes　it　hard　to　obtain　suitable　training　data　for　continuous　cell　selection　model　learning.　To　solve　this　problem,　we　model　the　spatiotemporal　correlations　in　the　collected　sparse　data,　and　then　design　various　methods　to　update　training　data　based　on　it.　Particularly,　these　methods　make　full　use　of　the　gradual　changes　of　data　in　time　and　space,　and　reasonably　transform　and　splice　sparse　data　at　different　moments.　Finally,　updated　training　data　is　fed　to　the　cell　selection　model　to　keep　it　up-to-date.　We　conduct　experimental　evaluations　by　performing　several　sensing　tasks　in　air　quality　monitoring.　The　results　show　that　our　proposed　methods　can　effectively　update　training　data　as　well　as　the　cell　selection　model.　Compared　with　several　baselines,　our　best　method　can　reduce　inference　error　by　more　than　10%　on　average.