As a part of natural climate variability,Near surface air temperature is an indispensable parameter that drives the energy and water exchanges among the hydrosphere, atmosphere and biosphere. Spatially and temporally resolved observations of near surface air temperatures are essential for understanding hydrothermal circulation at the land-atmosphere interface, and have had significant ecological impacts on many parts of the natural ecosystems. Given the ecological significance of near surface air temperature, the demand for accurate spatial data has risen greatly. Unfortunately, the gridded near surface air temperature data is generally limited by station coverage of meteorological observations, especially in extensive mountainous regions. Moreover, the uneven spatial distribution of meteorological stations may not effectively capture the true nature of the overall climate pattern. Given the strong correlation between land surface temperature and near surface air temperature, recent efforts have developed an alternative method for retrieving spatially continuous near surface air temperature from satellite-derived land surface temperature data sets. However, the degree of accuracy for current applications in near surface air temperature estimation still has a large room for improvement. Here we introduce a novel approach that combines High Accuracy Surface Modeling (HASM) with Geographically Weighted Regression (GWR) model for improving estimation of near surface air temperature in a data-fusion context. In this approach, application of fusion methods using Moderate Resolution Imaging Spectroradiometer (MODIS) products and ground-based observations was used. By fusing the MOD11C3 land surface temperature products and the air temperature data observed at 190 meteorological stations in Sichuan province, this study combines HASM with GWR model for improving estimation of near surface air temperature. To assess the feasibility of this modified model, we use 175 stations for model development and reserve15 for validation tests with three repetitions. The performance of combining HASM with GWR model (HASM-GWR) is also compared with multifactorial Geographically Weighted Regression (GWR) and Ordinary Least Squares (OLS) methods. The results indicated that the best estimation was found in HASM-GWR model. Specifically, the validation results from HASM-GWR model show that 72% of the estimated residual error is between -1 ℃ and 1 ℃, 90% is between -2 ℃ and 2 ℃, and the Root Mean Square Error (RMSE) reduces by 25.42% and 39.83% in comparison with other techniques. In addition, the near surface air temperature map obtained from HASM-GWR is better than that obtained by using other methods. Therefore, the proposed HASM-GWR model demonstrated an effective proficiency in near surface air temperature estimation, and it can be seen as an alternative to the popular data fusiontechniques.