Accurate　ocean　environment　perception　is　crucial　for　weather　and　climate　prediction.　Environmental　limitations　and　deployment　costs　constrain　satellite　and　buoy　real-time　observation,　leading　to　sparse　data　availability.　This　paper　proposes　a　novel　approach,　multimodal　fusion　-based　spatiotemporal　incremental　learning,　enhancing　the　ocean　environment　perception　under　sparse　observations.　This　method　uses　sparse　real-time　observations　to　comprehend,　reconstruct,　and　predict　the　full　environment.　First,　spatiotemporal　disentanglement　decouples　intrinsic　features　by　integrating　physical　principles　and　data　learning.　Subsequently,　incremental　extension　captures　the　dynamic　environment　through　stable　representation　updating　and　dynamic　behavior　learning.　Then,　multimodal　information　fusion　synergizes　multisource　intrinsic　features,　enabling　the　full　perception　of　the　ocean　environment.　Finally,　the　methodology　is　supported　by　convergence　analysis　and　error　boundary　evaluation.　Validation　with　global　sea　surface　temperature　and　western　Pacific　Ocean　highdimensional　temperature　datasets　demonstrates　its　potential　for　advancing　ocean　research　and　applications　using　sparse　real-time　observation.