Temperature Vegetation Dryness Index (TVDI) is an important tool that reflects agriculture dry situation by inverting soil moisture. The changes of energy balance and vegetation index are two main factors to influence the precision of the TVDI. The MODIS (Moderate….) data products, as RVI(Ratio Vegetation Index), NDVI(Normalized Difference Vegetation Index), EVI(Enhanced Vegetation Index), MSAVI(Modified Soil Adjusted Vegetation Index), and T_s (Land Surface Temperatures), are applied and the DEM (ASTER-GDEM) data are used to correct the T_s data for the reduction of the topographic influences by topographic relief. The TVDI is then employed by comparison of different vegetation index, where the TVDI is more sensitive to soil moisture. Thus the dry situation in the study area is analyzed during the plant growth time and compared by the synchronous meteorology data. The results indicate that: (1) terrain correction can effectively prevent the decrease of TVDI value from a lower surface temperature for a higher pixel. The correlation between T_s-NDVI index and measured values on May is compared, R< sup>2 will increase from 0.4634 to 0.5859 by terrain correction. It shows that the terrain corrected TVDI can improve effectively the estimation of soil moisture. (2) By comparing the correlation between T_s-NDVI, T_s-EVI, T_s-RVI, T_s-MSAVI and soil moisture,all the TVDIs present the negative correlations with soil moisture. The best correlations between the soil moisture and TVDIs can be always found, such as T_s-MSAVI in June, July and September 2005, T_s-EVI in May, and T_s-NDVI in August. Thus a TVDI feature space for different periods by these vegetation indexes are built for inversion of drought conditions. By comparison with agricultural meteorology, the results are acceptable. (3) Large area of the study area was humid from May to September 2005, drought occurred in the West on August, and humid was located in East on June. Therefore, compared with the measured data, the terrain corrected TVDI model is robust to eliminate the terrain and land cover influences to land surface temperature for inversion of soil moisture in the study area. And it is faithful to predict the agricultural drought condition in the study area during 2005 crop growth season.