This　paper　develops　a　new　crop　mapping　method　through　combined　utilization　of　both　time　and　frequency　information　based　on　wavelet　variance　and　Jeffries-Matusita　(JM)　distance　(CIWJ　for　short).　A　two-dimensional　wavelet　spectrum　was　obtained　from　datasets　of　daily　continuous　vegetation　indices　through　a　continuous　wavelet　transform　using　the　Mexican　hat　and　the　Morlet　mother　wavelets.　The　time-average　wavelet　variance　(TAWV)　and　the　scale-average　wavelet　variance　(SAWV)　were　then　calculated　based　on　the　wavelet　spectrum　of　the　Mexican　hat　and　the　Morlet　wavelet,　respectively.　The　class　separability　based　on　the　JM　distance　was　evaluated　to　discriminate　the　proper　period　or　scale　range　applied.　Finally,　a　procedure　for　criteria　quantification　was　developed　using　the　TAWV　and　SAWV　as　the　major　metrics,　and　the　similarity　between　unclassified　pixels　and　established　land　use/cover　types　was　calculated.　The　proposed　CIWJ　method　was　applied　to　the　middle　Hexi　Corridor　in　northwest　China　using　250-m　8-day　composite　moderate-resolution　imaging　spectroradiometer　(MODIS)　enhanced　vegetation　index　(EVI)　time　series　datasets　in　2012.　The　CIWJ　method　was　shown　to　be　efficient　in　crop　field　mapping,　with　an　overall　accuracy　of　83.6　%　and　kappa　coefficient　of　0.7009,　assessed　with　30　m　Chinese　Environmental　Disaster　Reduction　Satellite　(HJ-1)-derived　data.　Compared　with　methods　utilizing　information　on　either　frequency　or　time,　the　CIWJ　method　demonstrates　tremendous　potential　for　efficient　crop　mapping　and　for　further　applications.　This　method　could　be　applied　to　either　coarse　or　high　spatial　resolution　images　for　agricultural　crop　identification,　as　well　as　other　more　general　or　specific　land　use　classifications.