Land surface temperature (Ts) is influenced by soil background and vegetation growing conditions, and the combination of Ts and vegetation indices (Vis) can indicate the status of surface soil moisture content (SMC). In this study, Advanced Temperature Vegetation Dryness Index (ATVDI) used for monitoring SMC was proposed on the basis of the simulation results with agricultural climate model CUPID. Previous studies have concluded that Normalized Difference Vegetation Index (NDVI) easily reaches the saturation point, andLeaf Area Index (LAI) was then used instead of NDVI to estimate soil moisture content in the paper. With LAI-Ts scatter diagram established by the simulation results of CUPID model, how Ts varied with LAI and SMC was found. In the case of the identical soil background, the logarithmic relations between Ts and LAI were more accurate than the linear relations included in Temperature Vegetation Dryness Index (TVDI), based on which ATVDI was then developed. LAI-Ts scatter diagram with satellite imagery were necessary for determining the expression of the upper and lower logarithmic curves while ATVDI was used for monitoring SMC. Ts derived from satellite imagery were then transformed to the Tsvalue which has the same SMC and the minimum LAI in study area with look–up table. The measured SMC from the field sites in Weihe Plain, Shanxi Province, China, and the products of LAI and Ts (MOD15A2 and MOD11A2, respectively) produced by the image derived from Moderate Resolution Imaging Spectrometer (MODIS) were collected to validate the new method proposed in this study. The validation results shown that ATVDI (R2=0.62) was accurate enough to monitor SMC, and it achieved better result than TVDI.Moreover, ATVDI-derived result were Ts values with some physical meanings, which made it comparative in different periods. Therefore, ATVDI is a promising method for monitoring SMC in different time-spatial scales in agricultural fields.