This　paper　evaluated　the　spatiotemporal　non-stationarity　in　the　vegetation　dynamic　based　on　1-km　resolution　16-day　composite　Moderate　Resolution　Imaging　Spectroradiometer　(MODIS)　Enhanced　Vegetation　Index　(EVI)　datasets　in　China　during　2001-2011　through　a　wavelet　transform　method.　First,　it　revealed　from　selected　pixels　that　agricultural　crops,　natural　forests,　and　meadows　were　characterized　by　their　distinct　intra-annual　temporal　variation　patterns　in　different　climate　regions.　The　amplitude　of　intra-annual　variability　generally　increased　with　latitude.　Second,　parameters　calculated　using　a　per-pixel　strategy　indicated　that　the　natural　forests　had　the　strongest　variation　pattern　from　seasonal　to　semiannual　scales,　and　the　multiple-cropping　croplands　typically　showed　almost　equal　variances　distributed　at　monthly,　seasonal,　and　semiannual　scales.　Third,　spatiotemporal　non-stationarity　induced　from　cloud　cover　was　also　evaluated.　It　revealed　that　the　EVI　temporal　profiles　were　significantly　distorted　with　regular　summer　cloud　cover　in　tropical　and　subtropical　regions.　Nevertheless,　no　significant　differences　were　observed　from　those　statistical　parameters　related　to　the　interannual　and　interannual　components　between　the　de-clouded　and　the　original　MODIS　EVI　datasets　across　the　whole　country.　Finally,　12　vegetation　zones　were　proposed　based　on　spatiotemporal　variability,　as　indicated　by　the　magnitude　of　interannual　and　intra-annual　dynamic　components,　normalized　wavelet　variances　of　detailed　components　from　monthly　to　semiannual　scale,　and　proportion　of　cloud　cover　in　summer.　This　paper　provides　insightful　solutions　for　addressing　spatiotemporal　non-stationarity　by　evaluating　the　magnitude　and　frequency　of　vegetation　variability　using　monthly,　seasonal,　semiannual　to　interannual　scales　across　the　whole　study　area.