The released third-generation NDVI datasets in 2014, GIMMS NDVI3g, provide a data basis for quantifying recent regional vegetation dynamics over a sufficiently long term. The comparison between the new and old versions (GIMMS NDVIg, from 1981 to 2006) is necessary to link previous studies with future applications of GIMMS NDVI3g in monitoring vegetation activity trends and their responses to climate change. In this study, GIMMS NDVI3g was initially compared with GIMMS NDVIg in an evaluation of spatio-temporal patterns of seasonal vegetation changes in Xinjiang Province, China, at regional and pixel scales during overlapping periods from 1982 to 2006. The influences of climate change (including temperature, precipitation, potential evapotranspiration, and humidity index) on vegetation growth were then analyzed based on GIMMS NDVI3g and GIMMS NDVIg. To better understand the relationships between GIMMS NDVI3g and GIMMS NDVIg, NDVI trends and correlations between NDVI and climatic factors were calculated over multiple nested time series from 18 to 25 starting in 1982. The results indicated that most areas showed an approximate consistency in overall changing trends and correlations with climate variables for both datasets, but differences in many aspects should not be ignored. In most pixels, numerical values of GIMMS NDVI3g were larger than those of GIMMS NDVIg in the growing season, spring, summer, and autumn, particularly in summer, and also in those areas with dense vegetation. At a regional scale, the NDVI trends of GIMMS NDVI3g were smoother than those of GIMMS NDVIg in the growing season and all seasons, particularly in summer and longer periods. At the pixel scale, areas with a significant increase in GIMMS NDVI3g were less than those in GIMMS NDVIg, whereas this was not true in those areas with a significant decrease. The spatial patterns of correlations between GIMMS NDVI3g and four climate variables were approximately similar to those between GIMMS NDVIg and the climate variables, but there were some differences in the sensitivity of both datasets to climate change. Which dataset is more sensitive depends on climate variables and periods. In general, areas with significantly positive correlations between GIMMS NDVI3g and thermal factors were fewer than those of GIMMS NDVIg, whereas positive correlations between NDVI and moisture factors were greater in GIMMS NDVI3g than in GIMMS NDVIg. Integrated other ecological datasets, it is urgent to identify the similarities and differences between the two datasets and to establish a connection between them for reasonably monitoring vegetation dynamics using NDVI datasets.