The　global　water　environment　confronts　numerous　challenges,　e.g.,　water　pollution,　overexploitation,　and　ecological　degradation.　Comprehensive　protection　and　management　are　imperative　for　sustainable　water　resource　utilization.　Water　quality　predictions　provide　timely　warning　of　future　water　quality　problems　and　enable　early　action　to　avoid　deterioration.　As　science　and　technology　are　increasingly　applied　in　comprehensive　water　environment　management,　a　diverse　array　of　multimodal　data　is　gathered　from　various　sources,　including　remote　sensing　images　and　hydrological　time　series.　However,　current　water　quality　prediction　methods,　e.g.,　statistical,　machine　learning,　and　deep　learning　methods　fail　to　utilize　multimodal　data　to　enhance　their　accuracy　of　water　quality　prediction.　To　solve　the　above　problem,　this　work　proposes　a　multi-factor　and　long-term　water　quality　prediction　model　based　on　multimodal　data　fusion　named　Low-rank　Multimodal　Fusion　TimesNet　(LMF-TimesN　et),　It　first　extracts　features　from　hydrological　time　series　and　remote　sensing　images,　respectively.　Then,　they　are　fused　with　the　low-rank　multimodal　fusion　network　to　extract　diverse　information.　Finally,　TimesNet　is　adopted　to　integrate　fused　multimodal　water　environment　information　for　water　quality　prediction.　Experimental　results　on　a　real-world　dataset　show　that　LMF　-　TimesNet　achieves　higher　prediction　accuracy　and　generalization　ability　than　its　state-of-the-art　peers.　©　2024　IEEE.