With　the　deep　integration　of　remote　sensing　technology　and　artificial　intelligence,　the　human　demand　for　the　application　of　remote　sensing　data　has　become　increasingly　refined.　However,　single-modal　data　has　limitations　in　the　interpretation　of　complex　scenes.　Optical　imagery,　while　rich　in　spatial　information,　suffers　from　weather　dependencies;　Synthetic　Aperture　Radar　(SAR)　data　provides　all-weather　capability　but　lacks　intuitive　interpretability;　and　hyperspectral　data,　despite　its　detailed　spectral　signatures,　presents　challenges　in　data　redundancy　and　computational　complexity.　Therefore,　single-modal　data　is　difficult　to　fully　exploit　the　deeper　information　in　remote　sensing　images.　These　limitations　underscore　the　critical　need　for　more　advanced　analytical　approaches　that　can　overcome　the　constraints　of　single-modal　data　interpretation.　For　this　reason,　the　collaborative　analysis　of　multi-modal　data　has　become　a　key　way　to　enhance　the　interpretation　capability　of　remote　sensing　and　is　driving　the　further　development　of　the　remote　sensing　field.　By　integrating　complementary　data　sources　and　leveraging　their　synergistic　relationships,　multi-modal　approaches　enable　more　robust　and　comprehensive　scene　interpretation.　Among　various　multi-modal　strategies,　remote　sensing　image-text　cross-modal　understanding　has　gained　particular　prominence　as　it　establishes　a　vital　connection　between　remote　sensing　image　features　and　human　semantic　cognition.　This　framework　enhances　visual　feature　representations　with　the　help　of　text　semantic　information　and　achieves　cross-modal　information　complementarity,　which　significantly　improves　the　performance　of　remote　sensing　interpretation.　This　paper　provides　a　comprehensive　examination　of　remote　sensing　image-text　cross-modal　understanding,　which　is　categorized　into　four　main　types　of　tasks:　remote　sensing　image　captioning　(referred　to　as　image-to-text),　text-to-image　generation　(referred　to　as　text-to-image),　remote　sensing　image-text　alignment　(referred　to　as　image-text　alignment),　and　remote　sensing　visual　question　answering　(referred　to　as　image-text　dialogue).　From　the　perspective　of　cross-modal　transformation　of　images　and　text,　remote　sensing　image-text　cross-modal　understanding　includes　image-to-text　and　text-to-image.　In　terms　of　local　content　interaction,　remote　sensing　image-text　cross-modal　understanding　includes　image-text　alignment　and　image-text　dialogue.　This　paper　begins　with　a　thorough　review　of　the　historical　development　and　current　state　of　image-text　cross-modal　research.　The　research　progress　at　home　and　abroad　of　four　tasks,　namely　image-to-text,　text-to-image,　image-text　alignment,　and　image-text　dialogue,　is　reviewed.　The　key　technological　breakthroughs　in　the　fields　of　image-text　cross-modal　are　mainly　introduced.　On　this　basis,　the　in-depth　analysis　is　conducted　on　the　technical　difficulties　faced　by　the　four　tasks.　Then,　it　presents　a　detailed　analysis　of　commonly　used　public　datasets　and　evaluation　metrics　for　remote　sensing　image-text　cross-modal　understanding.　Finally,　it　summarizes　the　technical　challenges　in　this　field,　which　mainly　include　three　aspects:　modal　alignment　of　remote　sensing　images　and　text,　cross-modal　interpretability,　and　cross-modal　reasoning.　Based　on　the　proposed　problem　of　remote　sensing　image-text　cross-modal,　it　looks　forward　to　the　future　research　directions.　(1)　In-depth　mining　of　cross-modal　information　(2)　Construction　of　earth　science　knowledge　graph.　(3)　Human-computer　interaction.　(4)　Large-scale　remote　sensing　image-text　model.　(5)　Language　diversity.　(6)　Remote　sensing　multi-source　data.　This　comprehensive　investigation　not　only　synthesizes　current　research　but　also　provides　a　clear　roadmap　for　future　research　in　remote　sensing　image-text　cross-modal　understanding,　with　potential　implications　for　numerous　practical　applications　in　environmental　monitoring,　urban　planning,　and　disaster　management.　©　2025　Science　Press.　All　rights　reserved.