In this paper

a method based on NDVI-Ts space is proposed for Chinese land cover classification. The ten days composite ch4 and ch5 light temperatures for 1995 were used to estimate land surface temperature with split window method. Then the monthly land surface temperature and NDVI were produced with the maximum value composite from the 10-day composite Ts and NDVI. With the monthly Ts and NDVI

the monthly NDVI-Ts spaces were created. And pixel vector direction in the NDVI-Ts space was described with NTVA （Normalized Temperature Vegetation Angel）. Principal Components Analysis （PCA） was used to compress the 12 monthly NTVA images and three Principal Components were extracted. Fuzzy K-mean algorithm in clustering was used with the three Principal Components

summation of monthly NDVI and accumulated >0℃ Ts image to produce China land cover classification map. The result revealed that remarkable improvement for land cover classification can be reached with the combination of NDVI and land surface temperature..