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Meteorological elements are important for various fields related to human activities, including scientific research. Using the Tibetan Areas of West Sichuan Province (TAOWS) as an example, this study examined the estimation methods for near-surface air temperature (Ta), vapour pressure deficit (VPD), and atmospheric pressure (P) and their distribution characteristics in areas with complex terrains and sparse stations. An improved satellite-based approach, combining an artificial neural network and inverse distance weighting (ANN-IDW), is proposed for estimating Ta and VPD with high-accuracy under all weather conditions from Moderate Resolution Imaging Spectroradiometer (MODIS) data. The data of 41 meteorological stations in TAOWS and its adjacent areas were used for the training and validation of the ANN-IDW. For Ta and VPD, the mean absolute errors (MAEs) of the ANN-IDW are 1.45°C to 2.15°C and 0.54 hPa to 0.87 hPa, respectively. Also, the detailed features of the distribution of the estimated Ta and VPD are prominent and closely related to the terrain. The accuracy of the method was also verified indirectly. In addition, the improved method based on the existing method was applied for estimating P. The results confirm that (1) the ANN-IDW is suitable for estimating Ta and VPD in areas with complex terrain and sparse stations under all weather conditions; (2) the improved method is more suitable for estimating P at high-elevation. Moreover, the distribution characteristics of meteorological elements in TAOWS were also analysed. These elements influence agricultural production and animal husbandry and have a high application value. The results further show that topography is the most important factor affecting the spatial distribution and complexity of meteorological elements over complex terrains, but the degree of influence of topography varies greatly across different seasons. [ABSTRACT FROM AUTHOR]