As an important component of terrestrial ecosystems, vegetation plays an important role in climate regulation, soil and water conservation, and terrestrial carbon balance. The monitoring of vegetation growth and dynamics responsive to climate change therefore become quite necessary to understand global climate change. Based on the GIMMS NDVI and MODIS NDVI as well as a consistency check between these two datasets, this study examined the relationships between the spatiotemporal patterns of the NDVI and climate change over the black-soil area of the Northeast China from 1982 to 2016, using the unary linear regression model at both the pixel and regional scales. Our results showed that, at the regional scale, the NDVI during the growing season presented three different trends across these years (i.e., firstly with an increase, then a decrease, and finally an increase). Regionally, the vegetation growth was jointly affected by temperature and precipitation, which showed significant seasonal changes. By contrast, at the pixel scale, the NDVI exhibited a general increasing trend. The main vegetation types characterized by an increasing trend included grasslands, forests, and crops; and the cities that experienced significant vegetation growth are Hegang, Suihua, and Changchun. The mean NDVI had a close correlation with temperature and precipitation over the same period. Over the regions that are mainly covered by arable lands, the vegetation NDVI was positively related to air temperature, while it had a significant positive correlation with precipitation over the edges of the plains and mountains, where the dominant vegetation types are forests and grasslands.