Earth　observation　(EO)　data　cubes　have　gained　significance　for　their　potential　in　providing　comprehensive　insights　into　dynamic　earth　phenomena.　However,　a　significant　challenge　in　their　utilization　is　the　interoperability　among　EO　data　cubes　with　varying　dimensionalities.　Existing　efforts　have　primarily　focused　on　enhancing　analysis　ready　data　cubes,　yet　cross-dimensional　adaptivity　remains　relatively　unexplored.　This　study　proposes　a　novel　approach　to　address　this　issue.　The　proposed　method　is　based　on　a　3D　adaptive　data　cube　model　constructed　in　a　3D　adaptive　space.　The　main　objective　is　to　achieve　interoperability　between　EO　data　cubes　of　different　dimensions　while　adhering　to　common　geospatial　standards.　The　core　methodology　involves　cross-dimensional　mapping,　considering　a　3D　adaptive　space　containing　spatial　axes　and　adaptive　axes.　Importantly,　the　method　applies　to　data　cubes　with　the　same　coordinate　reference　system,　resolution,　and　data　type.　To　enable　cross-dimensional　mapping,　a　key　requirement　is　the　existence　of　a　mapping　function　between　the　multidimensional　space　domain　and　the　3D　adaptive　space　domain.　When　these　conditions　are　met,　data　cubes　with　three　or　more　dimensions　can　be　interoperable.　This　becomes　feasible　by　applying　suitable　serialization　algorithms.　This　study　demonstrates　that　data　cubes　can　be　successfully　mapped　into　the　proposed　3D　adaptive　data　cube,　achieving　interoperability　under　specific　conditions.　This　finding　has　significant　implications　in　the　field　of　EO　data　analysis,　enabling　seamless　interaction　between　data　cubes　with　different　dimensions.　The　method　not　only　facilitates　cross-dimensional　adaptability　but　also　aligns　with　mainstream　geospatial　service　standards.　It　is　particularly　suitable　for　multidimensional　geospatial　raster　services　based　on　the　OGC　WCS　2.0　standard.　In　conclusion,　this　study　addresses　a　critical　challenge　in　EO　data　analysis　by　proposing　a　3D　adaptive　data　cube　model　that　promotes　interoperability　between　data　cubes　of　varying　dimensions.