Abstract: Based on daily precipitation data from 60 national meteorological stations in Xinjiang, China, from 1981 to 2020, satellite remote sensing data of the normalized difference vegetation index (NDVI) from NOAA (National Oceanic and Atmospheric Administration), and satellite remote sensing data of snow cover distribution from NOAA from 2000 to 2020, this study analyzes the spatiotemporal distribution characteristics of vegetation cover in the region. Furthermore, correlation analysis is conducted to explore relevant climatic factors. The results show that the overall NDVI in Xinjiang in July and August exhibited an upward trend from 1981 to 2020, with the increasing trend slowing down during 2000–2020. The annual average daily precipitation rate varied between ±0.1 mm (10 a)⁻¹, with substantial differences across areas and seasons. The annual snowmelt rate (SMR) in southern Xinjiang exhibited a downward trend in general during 2000–2020, while northern Xinjiang exhibited a weak upward trend. Correlation analysis indicated substantial variations in the correlation between precipitation and NDVI across different time periods. In flat terrain areas, the NDVI change rate at most stations exhibited a higher correlation with nonwinter precipitation; however, at other stations, NDVI was observed to be mainly correlated with winter precipitation and SMR, with these stations generally located at the foothills of mountains. This indicates that, on the one hand, the vegetation index in this region is substantially influenced by precipitation, consistent with the relationship between climate and vegetation in arid and semi-arid regions. On the other hand, winter snowfall and mountain snowmelt also play an important role in vegetation growth in foothill areas. Based on this study’s results, we speculate that the increasing trend of annual snowmelt during the past 40 years may be one of the important reasons for the annual improvement in vegetation cover in these areas. The findings of this study provide observational evidence from the perspective of vegetation cover for climate change research in Xinjiang.