Air temperature is an important attribute for evaluating the living environment, and its studies and observations are closely related to human production and life. Air temperature observation data is of great significance for the study of hydrology, environment, ecology, and climate change. Traditional description of large-scale air temperature is generally obtained through meteorological stations. As affected by land surface condition and atmospheric state, the air temperature is spatially heterogeneous. However, due to the sparse spatial distribution of meteorological station sites, the data obtained from these meteorological stations cannot accurately describe the continuous spatial variation of air temperature across large areas. Hence, accurate inversion of near-surface air temperature based on remote sensing data is regarded as an effective and reasonably practicable solution. There are already some studies about obtaining spatially continuous near-surface air temperature using land surface temperature and other remote sensing data. In this study, we have used the remote sensing data, specifically the precise surface coverage type and spatially continuous soil moisture data, as the new input to improve the accuracy of temperature inversion. On this basis, we built a near-surface air temperature spatialization model using Land Surface Temperature (LST), land cover, soil moisture, land surface temperature, NDVI, DEM, aspect, and slope as the influencing factors. In order to fit the complex relationship between air temperature and its influencing factors, we chose four widely used machine learning algorithms and compared their accuracy to select the most reasonable model. At the same time, we also validated the results and evaluated the contribution of the influencing factors. Based on the results of the designed experiments, we found that precise surface cover type and spatially continuous soil moisture data played the most important role in near-surface air temperature spatialization model. The surface cover type has the greatest influence on the near-surface air temperature, and soil moisture is the most active influencing factor. The model validation results showed that the spatialization model has a relatively high accuracy, with an R²value close to 0.85, and a RMSE of 0.5℃. Comparing with traditional methods, the results of near-surface air temperature spatialization model in our study could express more refined spatial distribution pattern. The high precision near-surface air temperature inversion model proposed by our research is expected to provide effective data support to the study on the dynamic monitoring of agricultural meteorological disasters, simulation of crop growth processes, and analysis of regional climate change.