In recent years, global warming and intense human activity have been responsible for significantly altering vegetation dynamics on the Mongolian Plateau. Understanding the long-term vegetation dynamics in this region is important to assess the impact of these changes on the local ecosystem. Long-term (1982–2015), satellite-derived normalized difference vegetation index (NDVI) datasets were used to analyse the spatio-temporal patterns of vegetation activities using linear regression and the breaks for additive season and trend methods. The links between these patterns and changes in temperature, precipitation (PRE), soil moisture (SM), and anthropogenic activity were determined using partial correlation analysis, the residual trends method, and a stepwise multiple regression model. The most significant results indicated that air temperature and potential evapotranspiration increased significantly, while the SM and PRE had markedly decreased over the past 34 years. The NDVI dataset included 71.16% of pixels showing an increase in temperature and evaporation during the growing season, particularly in eastern Mongolia and the southern border of the Inner Mongolia Autonomous region, China. The proportion indicating the breakpoint of vegetation dynamics was 71.34% of pixels, and the trend breakpoints mainly occurred in 1993, 2003, and 2010. The cumulative effects of PRE and SM in the middle period, coupled with the short-term effects of temperature and potential evapotranspiration, have had positive effects on vegetation greening. Anthropogenic factors appear to have positively impacted vegetation dynamics, as shown in 81.21% of pixels. We consider rapid economic growth, PRE, and SM to be the main driving factors in Inner Mongolia. PRE was the main climatic factor, and combined human and livestock populations were the primary anthropogenic factors influencing vegetation dynamics in Mongolia. This study is important in promoting the continued use of green projects to address environmental change in the Mongolian Plateau.

With China as the study area, MODIS MOD14A1 and MCD12Q1 products were used to derive daily crop residue burning spots from 2014 to 2015. After vectorization of crop residue burning pixels and with the use of fishnet, burning density distribution maps were eventually completed. Meanwhile, the daily air quality data from 150 cities in 2014 and 285 cities in 2015 were used to obtain daily and monthly PM2.5 distribution maps with the Kriging interpolation. The results indicate that crop residue burning occurs in a seasonal pattern, and its spatial distribution is closely related to farming activities. The annual PM2.5 in China decreased 11.81% from 2014 to 2015, and the distribution of PM2.5 in China's east and north is always higher than in China's west and south. Furthermore, the changes in PM2.5 exhibit a hysteresis after crop residue burning in summer and autumn-winter. Regarding summer crop residue burning in China's middle-east, the r between crop residue burning spots and PM2.5 is 0.6921 (P < 0.01) in 2014 and 0.5620 (P < 0.01) in 2015, while the correlation coefficient of autumn-winter crop residue burning in China's northeast is slightly lower with an r of 0.5670 (P < 0.01) in 2014 and 0.6213 (P < 0.01) in 2015. In autumn-winter, crop residue burning can induce evident PM2.5 increase in China's northeast, and that is more obvious than summer crop residue burning in China's middle-east. Furthermore, when data of summer and autumn-winter crop residue burning from 2014 to 2015 are compared, we can see that the change in number of crop residue burning spots significant changes PM2.5 in these regions. Both the summer and autumn-winter crop residue burning areas presented spatial consistency with high PM2.5. By contrast, the results from many aspects indicated that the crop residue burning in spring did not cause a notable change of PM2.5.