Spatiotemporal　explicit　information　on　paddy　rice　distribution　is　essential　for　ensuring　food　security　and　sustainable　environmental　management.　Paddy　rice　mapping　algorithm　through　the　Combined　Consideration　of　Vegetation　phenology　and　Surface　water　variations　(CCVS)　has　been　efficiently　applied　based　on　the　8 day　composites　time　series　datasets.　However,　the　great　challenge　for　phenology-based　algorithms　introduced　by　unpromising　data　availability　in　middle/high　spatial　resolution　imagery,　such　as　frequent　cloud　cover　and　coarse　temporal　resolution,　remained　unsolved.　This　study　addressed　this　challenge　through　developing　an　automatic　and　Adaptive　paddy　Rice　Mapping　Method　(ARMM)　based　on　the　cloud　frequency　and　spectral　separability.　The　proposed　ARMM　method　was　tested　on　the　Landsat　8　Operational　Land　Imager　(OLI)　image　(path/row　118/028)　in　the　Songnen　Plain　in　Northeast　China　in　2015.　First,　the　whole　study　region　was　automatically　and　adaptively　subdivided　into　undisturbed　and　disturbed　regions　through　a　per-pixel　strategy　based　on　Landsat　image　data　availability　during　key　phenological　stage.　Second,　image　objects　were　extracted　from　approximately　cloud-free　images　in　disturbed　and　undisturbed　regions,　respectively.　Third,　phenological　metrics　and　other　feature　images　from　individual　or　multiple　images　were　developed.　Finally,　a　flexible　automatic　paddy　rice　mapping　strategy　was　implemented.　For　undisturbed　region,　an　object-oriented　CCVS　method　was　utilized　to　take　the　full　advantages　of　phenology-based　method.　For　disturbed　region,　Random　Forest　(RF)　classifier　was　exploited　using　training　data　from　CCVS-derived　results　in　undisturbed　region　and　feature　images　adaptively　selected　with　full　considerations　of　spectral　separability　and　the　spatiotemporal　coverage.　The　ARMM　method　was　verified　by　473　reference　sites,　with　an　overall　accuracy　of　95.77%　and　kappa　index　of　0.9107.　This　study　provided　an　efficient　strategy　to　accommodate　the　challenges　of　phenology-based　approaches　through　transferring　knowledge　in　parts　of　a　satellite　scene　with　finer　time　series　to　targets　(other　parts)　with　deficit　data　availability.　©　2017　Elsevier　B.V.