Tea　trees　(Camellia　sinensis),　a　quintessential　homestead　agroforestry　crop　cultivated　in　over　60　countries,　hold　significant　economic　and　social　importance　as　a　vital　specialty　cash　crop.　Accurate　nationwide　crop　data　is　imperative　for　effective　agricultural　management　and　resource　regulation.　However,　many　regions　grapple　with　a　lack　of　agroforestry　cash　crop　data,　impeding　sustainable　development　and　poverty　eradication,　especially　in　economically　underdeveloped　countries.　The　large-scale　mapping　of　tea　plantations　faces　substantial　limitations　and　challenges　due　to　their　sparse　distribution　compared　to　field　crops,　unfamiliar　characteristics,　and　spectral　confusion　among　various　land　cover　types　(e.g.,　forests,　orchards,　and　farmlands).　To　address　these　challenges,　we　developed　the　Manual　management　And　Phenolics　substance-based　Tea　mapping　(MAP-Tea)　framework　by　harnessing　Sentinel-1/2　time　series　images　for　automated　tea　plantation　mapping.　Tea　trees,　exhibiting　higher　phenolic　content,　evergreen　characteristics,　and　multiple　shoot　sprouting,　result　in　extensive　canopy　coverage,　stable　soil　exposure,　and　radar　backscatter　signal　interference　from　frequent　picking　activities.　We　developed　three　phenology-based　indicators　focusing　on　phenolic　content,　vegetation　coverage,　and　canopy　texture　leveraging　the　temporal　features　of　vegetation,　pigments,　soil,　and　radar　backscattering.　Characteristics　of　biochemical　substance　content　and　manual　management　measures　were　applied　to　tea　mapping　for　the　first　time.　The　MAP-Tea　framework　successfully　generated　China＇s　first　updated　10　m　resolution　tea　plantation　map　in　2022.　It　achieved　an　overall　accuracy　of　94.87%　based　on　16,712　reference　samples,　with　a　kappa　coefficient　of　0.83　and　an　F1　score　of　85.63%.　The　tea　trees　are　typically　cultivated　in　mountainous　and　hilly　areas　with　a　relatively　low　planting　density　(averaging　about　10%).　Alpine　tea　trees　exhibited　a　notably　dense　concentration　and　dominance,　mainly　found　in　regions　with　elevations　ranging　from　700　m　to　2000　m　and　slopes　between　2　degrees　to　18　degrees.　The　areas　with　low　altitudes　and　slopes　hold　the　largest　tea　plantation　area　and　output.　As　the　slope　increased,　there　was　a　gradual　decline　in　the　dominance　of　tea　areas.　The　results　suggest　a　good　potential　for　the　knowledge-based　approaches,　combining　biochemical　substance　content　and　human　activities,　for　national-scale　tea　plantation　mapping　in　complex　environment　conditions　and　challenging　landscapes,　providing　important　reference　significance　for　mapping　other　agroforestry　crops.　This　study　contributes　significantly　to　advancing　the　achievement　of　the　Sustainable　Development　Goals　(SDGs)　considering　the　crucial　role　that　agroforestry　crops　play　in　fostering　economic　growth　and　alleviating　poverty.　The　first　10m　national　Tea　tree　data　products　in　China　with　good　accuracy　(ChinaTea10m)　are　publicly　accessed　at　https://doi.org/10.6084/m9.figshare　.25047308.