Taking winter wheat in China as an example, large-scale crop planting areas automatic identification methods were researched based on time-series of MODIS - NDVI datasets. The characteristics of NDVI time series of winter wheat in China were firstly analyzed, and then the threshold values of extracting crop planting area were set and the extraction models of winter wheat were established, finally spatial distribution of winter wheat of 2010 -2011 was obtained, the results showed that the extraction accuracy of winter wheat planting area was more than 81% compared with the average statistical data obtained in years. MODIS data of China in 2011 were used to monitor the growth condition of winter wheat, and the growth condition was compared with the average crop growth of the last five years. Results show that winter wheat growth condition has different characteristics both in spatial and temporal.%利用MODIS-NDVI数据,以中国冬小麦主产区为例,探讨了基于遥感影像全覆盖的大尺度冬小麦种植面积遥感综合自动识别及长势监测的方法.通过分析冬小麦的种植结构、物候历特征及其生物学特性和时序NDVI曲线特征,确定了冬小麦信息提取的NDVI阈值,建立了冬小麦面积提取模型,并最终获取了2010-2011年中国农情遥感监测中冬小麦长势监测所需的空间分布数据,与多年平均统计数据比较,总体精度达到81％以上.基于提取的冬小麦面积信息空间分布数据,利用MODIS-NDVI差值模型,对冬小麦2011年的长势进行监测.结果表明,与近5年平均状况对比,2011年冬小麦在其整个生育期内长势基本与常年持平,但时空分布差异较大.
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