Timely　and　accurate　monitoring　of　cropping　intensity　(CI)　is　essential　to　help　us　understand　changes　in　food　production.　This　paper　aims　to　develop　an　automatic　Cropping　Intensity　extraction　method　based　on　the　Isolines　of　Wavelet　Spectra　(CIIWS)　with　consideration　of　intra-class　variability.　The　CIIWS　method　involves　the　following　procedures:　(1)　characterizing　vegetation　dynamics　from　time-frequency　dimensions　through　a　continuous　wavelet　transform　performed　on　vegetation　index　temporal　profiles;　(2)　deriving　three　main　features,　the　skeleton　width,　maximum　number　of　strong　brightness　centers　and　the　intersection　of　their　scale　intervals,　through　computing　a　series　of　wavelet　isolines　from　the　wavelet　spectra;　and　(3)　developing　an　automatic　cropping　intensity　classifier　based　on　these　three　features.　The　proposed　CIIWS　method　improves　the　understanding　in　the　spectral-temporal　properties　of　vegetation　dynamic　processes.　To　test　its　efficiency,　the　CIIWS　method　is　applied　to　China＇s　Henan　province　using　250　m　8　days　composite　Moderate　Resolution　Imaging　Spectroradiometer　(MODIS)　Enhanced　Vegetation　Index　(EVI)　time　series　datasets.　An　overall　accuracy　of　88.9%　is　achieved　when　compared　with　in-situ　observation　data.　The　mapping　result　is　also　evaluated　with　30　m　Chinese　Environmental　Disaster　Reduction　Satellite　(HJ-1)-derived　data　and　an　overall　accuracy　of　86.7%　is　obtained.　At　county　level,　the　MODIS-derived　sown　areas　and　agricultural　statistical　data　are　well　correlated　(r2　=　0.85).　The　merit　and　uniqueness　of　the　CIIWS　method　is　the　ability　to　cope　with　the　complex　intra-class　variability　through　continuous　wavelet　transform　and　efficient　feature　extraction　based　on　wavelet　isolines.　As　an　objective　and　meaningful　algorithm,　it　guarantees　easy　applications　and　greatly　contributes　to　satellite　observations　of　vegetation　dynamics　and　food　security　efforts.　©　2016　Elsevier　B.V.