Updated　spatiotemporal　explicit　data　on　cropping　system　is　vital　for　ensuring　the　implementation　of　the　national　food　security　strategy　and　reasonable　cropping　structures.　Time　series　analysis　techniques　are　playing　a　more　important　role　in　agricultural　remote　sensing　along　with　the　continuously　improved　quality　of　remote　sensing　time　series　images.　This　paper　analyzes　main　progresses　and　challenges　in　the　field　of　cropping　systems　mapping　using　time　series　images　from　three　aspects:　mapping　framework,　remote　sensing　feature　parameters,　and　data　products.　We　find　that:　(1)　The　current　cropping　system　mapping　framework　which　mainly　includes　cropping　intensity　and　agricultural　planting　structures,　needs　to　cope　with　the　problems　of　pre-requirements　of　cropland　distribution　data　with　high-quality.　However,　the　existing　land　use/cover　data　could　not　fully　fulfil　this　requirement　due　to　the　complex　spectral　characteristics　of　cropland　introduced　by　multiple　cropping　systems　over　large　regions.　It　is　difficult　to　accurately　derive　information　on　cropping　intensity　using　traditional　time　series　vegetation　indices　datasets.　Specifically,　cropland　fallow/abandonment　in　humid　regions　might　be　misclassified　as　single　crop　due　to　its　corresponding　high　values　of　vegetation　indices.　Cropland　abandonment　and　fallow　are　not　negligible　in　recent　decades　and　need　further　investigations,　especially　in　China;　(2)　Novel　multi-dimensional　spectral　indices　based　on　red-edge　and　short-wave　near-infrared　bands　are　efficient　in　revealing　the　crop　growth　processes.　Great　progresses　have　been　made　in　crop　mapping　in　recent　years.　However,　crop　mapping　at　large　scale　is　challenged　by　the　minor　differences　among　different　crops　as　well　as　distinct　heterogeneity　within　the　same　crop　across　different　regions　and　multiple　years;　(3)　There　are　increasing　available　remote　sensing　products　of　cropping　intensity　from　national　to　global　scale,　however,　the　timeliness　and　spatiotemporal　continuity　need　to　be　further　improved;　(4)　Except　for　a　few　countries　in　North　America　and　Europe,　crop　distribution　maps　at　national　scale　are　not　fully　available　or　limited　to　several　staple　crops　with　coarse　resolution.　There　is　a　deficiency　of　finer　datasets　on　cropping　systems　at　large　scale,　especially　in　the　complex　multi-cropped　regions.　Fortunately,　new　technologies　(i.e.,　cloud　computing　platform　and　deep　learning　algorithms)　and　emerging　multi-sources　remote　sensing　data　with　higher　spatial,　spectral,　and　temporal　resolution　provide　great　opportunities　for　spatiotemporally　continuously　detecting　changes　in　cropping　system　at　large　scale.　Future　research　should　be　focused　on　the　following　directions.　First,　we　could　improve　the　research　strategy　by　developing　an　integrated　mapping　framework　for　directly　deriving　information　on　cropland　and　cropping　patterns　without　relying　on　existing　cropland　distribution　data.　Second,　we　need　to　enrich　the　phenological　features　through　exploring　multiple-dimensional　and　less　exploited　spectral　indices,　such　as　the　pigment　indices,　soil　indices,　nitrogen　indices,　and　dry　matter　indices.　Finally,　we　can　develop　spatiotemporal　continuous　change　detection　techniques　for　automatically　tracking　changes　in　cropping　systems　at　multiple　years　and　large　scale.　©　2022,　Science　Press.　All　right　reserved.