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The temperature-vegetation drought index (TVDI) based on the surface temperature-vegetation index feature space has very important scientific and practical significance in drought monitoring and farmland irrigation. But traditional methods cannot accurately reflect the real surface water-heat energy exchange and make the soil moisture estimation have great uncertainty. Based on the equation of surface energy balance and the introduction of improved vegetation coverage parameter, a theoretical dry-wet edge endpoint selection method and a TVDI model based on the surface temperatureimproved vegetation coverage feature space were constructed, which broadened the application range of TVDI in drought monitoring and soil moisture estimation by improving vegetation coverage parameters to a certain extent to avoid restrictions on the types of surface coverage. MODIS remote sensing image data and ground observations were used to estimate the soil moisture of wheat field in Yangling District, Shaanxi Province. The results showed that the correlation coefficient between the TVDI calculated from the theoretical wet edge and the measured soil moisture was about -0.700, and the root mean square error was not more than 0.060cm3/cm3. In addition, the estimated soil moisture values of DOY088 and DOY112 both had good fitting relationship with the measured soil moisture values, especially the inversion results of DOY088 were closer to the actual surface conditions with correlation coefficient of -0.715 and root mean square error of 0.029cm3/cm3. Meanwhile, the scatter distribution of DOY112 was much more dispersed than that of DOY088. Therefore, this method can avoid the limitation of the surface vegetation coverage that must include bare soil, partial vegetation and full vegetation coverage in the estimation of traditional feature space dry and wet edges, realize remote sensing inversion of real soil moisture and monitor the actual wet and dry conditions of the ground.