Reliable　spatiotemporal　crop　data　are　vital　for　sustainable　agricultural　management.　However,　efficient　algorithms　that　can　be　automatically　applied　to　large　regions　are　scarce,　especially　for　cash　crops,　since　it　is　hard　to　distinguish　their　uniqueness　merely　from　temporal　profiles　of　traditional　vegetation　indices.　The　efficiency　of　knowledge-based　temporal　features　and　red-edge　pigment　indices　in　characterizing　crop　growth　has　been　reported　in　the　literature,　but　the　potential　of　combined　applications　in　identifying　crops　has　not　been　validated　yet.　This　study　fills　this　gap　by　developing　a　knowledge-based　automated　Peanut　mapping　Algorithm　with　a　combined　consideration　of　crop　Phenology　and　Pigment　content　variations　(PAPP).　Peanut　crop　has　earlier　and　longer　flowering　stages　compared　to　other　crops　such　as　paddy　rice　and　maize.　Peanut　fields　are　distinguished　with　less　variations　in　anthocyanin　and　chlorophyll　as　well　as　higher　carotenoid　concentrations.　Herein,　three　phenology　and　pigment-based　indicators　were　proposed　for　peanut　mapping　by　exploring　the　concentration　and　variations　of　the　chlorophyll,　anthocyanin　and　carotenoid　indices,　respectively.　This　PAPP　algorithm　was　validated　over　large　regions　(around　250　thousand　km(2)　cropland)　covering　three　provinces　of　Northeast　China　using　Sentinel-2　time-series　images.　The　results　reported　that　there　was　8,371　km(2)　peanut　area　in　Northeast　China　in　2018,　concentrated　in　the　western　Jilin　and　Liaoning　provinces.　Validation　from　the　1,102　field　survey　sites　revealed　overall　accuracies　of　94%,　with　a　kappa　index　of　0.87　and　F-1　score　of　0.91.　The　PAPP　algorithm　was　not　sensitive　to　thresholding,　and　a　high　classification　accuracy　could　be　obtained　once　the　threshold　of　one　indicator　was　roughly　defined.　The　thresholds　could　be　determined　based　on　the　proportions　of　staple　crops　(i.e.　paddy　rice　and　maize)　using　the　historical　agricultural　statistical　data　since　peanut　fields　either　show　the　least　or　largest　values　in　these　three　proposed　indicators.　The　PAPP　algorithm　demonstrates　the　capabilities　of　automatic　peanut　mapping　over　large　regions　with　no　requirements　of　further　training　and　modifications.　This　study　makes　contributions　to　a　sustainable　agricultural　management　society　given　the　potential　significant　role　of　legume　crops　in　co-delivering　food　security　and　adapting　to　climate　change.