Large-scale　high-resolution　water　body　(WB)　extraction　is　one　of　the　research　hotspots　in　remote　sensing　image　processing.　However,　accurate　training　labels　for　various　WBs　at　Very-High-Resolution　(VHR)　are　extremely　scarce.　Considering　that　low-resolution　(LR)　images　and　labels　are　more　easily　accessible,　the　challenge　lies　in　fully　leveraging　LR　data　to　guide　high-precision　WB　extraction　from　VHR　images.　To　address　this　issue,　we　propose　a　novel　cross-scale　CSW-SAM　algorithm　based　on　SAM2,　which　learns　spectral　information　of　WBs　from　easily　accessible　10　m　resolution　LR　images　and　maps　it　to　0.3　m　resolution　VHR　remote　sensing　images　for　high-precision　WB　segmentation.　In　addition　to　fine-tuning　the　decoder,　we　enhance　the　encoder＇s　ability　to　effectively　learn　the　mapping　relationship　between　images　of　different　resolutions　by　Adapter　Tuning.　We　have　designed　the　Automated　Clustering　Layer　(ACL)　based　on　the　principle　of　feature　similarity　and　local　structure　information　clustering,　to　enhance　the　performance　of　SAM-based　methods　in　cross-scale　WB　segmentation.　To　validate　the　robustness　and　generalization　ability　of　the　proposed　CSW-SAM,　we　conducted　extensive　experiments　on　both　a　self-constructed　cross-scale　WB　dataset　and　the　publicly　available　GLH-Water　dataset.　The　results　confirm　that　CSW-SAM　achieves　strong　performance　across　datasets　with　diverse　WB　conditions,　demonstrating　its　potential　for　scalable　and　low-cost　VHR　WB　mapping.　Additionally,　the　model　can　be　generalized　with　minimal　cost,　making　it　highly　promising　for　large-scale　global　VHR　WB　mapping.　©　2025　The　Author(s)