利用NDVI-Ts空间进行全国土地覆盖分类的方法。该方法利用1995年NOAA 10天合成的ch4、ch5通道亮温，先计算出陆地表面温度(Ts)，然后用最大值合成法计算每月的最大Ts和NDVI，以每月最大Ts和NDVI建立NDVI-Ts空间。根据像素点(NDVI，Ts)在空间中的位置矢量，求出矢量在空间中的方向角度，并作归一化处理，得到温度植被角度(NTVA)。对12个月NTVA做主成分变换提取前三个主分量，辅以全年总NDVI和大于O℃ Ts积温，用模糊K-均值法进行全国土地覆盖分类。研究结果表明，基于NDVI-Ts空间的NTVA与NDVI、Ts一起作为分类特征在土地覆盖分类中具有较高的分类精度，能够取得较好的分类效果。

In this paper, a method based onNDVI-Tsspace is proposed for Chinese land cover classification. The ten days\ncomposite ch4 and ch5 light temperatures for 1995 were used to estimate land surface temperature with split window method.\nThen the monthly land surface temperature andNDVIwere produced with the maximum value composite from the 10-day\ncompositeTsandNDVI. WiththemonthlyTsandNDVI, themonthlyNDVI-Tsspaceswere created. And pixel vector direction\nin theNDVI-Tsspace was described withNTVA(Normalized Temperature Vegetation Angel). Principal Components Analysis\n(PCA) was used to compress the 12 monthlyNTVAimages and three Principal Components were extracted. FuzzyK-mean\nalgorithm in clusteringwas used with the three Principal Components, summation of monthlyNDVIand accumulated >0℃Ts\nimage to produce China land cover classification map. The result revealed that remarkable improvement for land cover\nclassification can be reached with the combination ofNDVIand land surface temperature..