As one of the main components of terrestrial ecosystems, vegetation plays a key role in landscape structures and ecological services. Monitoring vegetation dynamics and their responses to climate change is beneficial to understanding ecological processes and designing adaptive management strategies. The GIMMS NDVI dataset from 1982 to 2006 is available for long-term NDVI trend analysis,and is widely used at global,regional,and local scales. Comparisons between the GIMMS NDVI dataset and new products retrieved from new sensors should be conducted to link existing studies with future applications of other NDVI products in monitoring vegetation activity change. The MODIS NDVI dataset is referred to as the successor and improvement to the GIMMS NDVI time series. Based on a data consistency test, the time sequences of the GIMMS NDVI dataset were extended to 2012 with the MODIS NDVI dataset (20002012). Then, we analyzed the trends of the NDVI anomalies and climatic factors (temperature,precipitation,reference crop evapotranspiration,and humidity index) during 19822012 for growing season, spring, summer, and autumn. In addition, the response of vegetation to climate change was explored. The results showed that the GIMMS and MODIS NDVI data were very consistent at a regional scale, whereas at pixel scales, the spatial pattern of correlation between GIMMS and MODIS NDVI was significantly different. There was highly significant positive correlation between GIMMS and MODIS NDVI in northern Xinjiang, and poor correlation in southern Xinjiang. The areas with poor correlation were mainly distributed in deserts and alpine regions. In general, the two datasets are consistent and can be combined to expand the length of the NDVI time series. The vegetation greenness in growing season, spring, summer, and autumn increased significantly from 1982 to 2012 in Xinjiang. The NDVI in growing season increased significantly from 1982 to 1998,then decreased significantly from 1998 to 2012;this trend was also observed in summer and autumn seasons. Similar to the changes in greenness at regional scales, the percentages of land areas experiencing positive anomalies also increased significantly during 19822012. The area bearing extreme and large anomalies (both positive and negative) of vegetation greenness generally decreased from 1982 to 2012, and moderate anomalies (both positive and negative) mostly increased from 1982 to 2012. The NDVI change tends to be gradually stable in study areas. The temperature and reference crop evapotranspiration increased significantly, and precipitation and humidity increased slightly over the past 31 years for all seasons. Vegetation growth in growing season was inhibited by both moisture and thermal conditions in Xinjiang, but the responses of vegetation to climate varied seasonally. The thermal was the primary climatic driver of vegetation changes in spring and autumn,and water resource affected plant growth in summer. The reduction of NDVI from 1998 to 2012 in the growing season, summer, and autumn was mainly due to drought stress, which was strengthened by warming in all seasons, and by reduced precipitation in growing season, spring,and summer. The long-term and consistent NDVI datasets offer a cheap,verifiable,and viable way to quickly detect change in vegetation, supporting managers in their effort to design and apply adaptive management strategies.