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Vegetation dynamics, particularly vegetation growth, are often used as indicators of potential grassland degradation. Grassland vegetation growth can be monitored using remotely sensed data, which has rapid and broad coverage. Grassland ecosystems are an important component of the regional landscape. In this study, we developed an applicable method for monitoring grassland growth. The dynamic variation in the grassland was analysed using Moderate Resolution Imaging Spectroradiometer (MODIS) data. The normalized difference vegetation index (NDVI) was calculated from 2001 to 2010 during the grassland growing season. To evaluate the grassland growth, the use of the growth index (GI) was proposed. According to the GI values, five growth grades were identified: worse, slightly worse, balanced, slightly better, and better. We explored the spatial-temporal variation of grassland growth and the relationship between grassland growth and meteorological factors (i.e. precipitation and temperature factors). Our results indicated that, compared with the multi-year average, the spatial-temporal variation of grassland growth was significantly different between 2001 and 2010. The vegetation growth was worse in 2009 compared with the multi-year average. A GI of worse’ accounted for 66.73% of the area. The vegetation growth in 2003 was the best of the years between 2001 and 2010, and a better GI accounted for 58.08% of the area in 2003. The GI from 2004 to 2008 exhibited significant fluctuations. The correlation coefficient between the GI and precipitation or temperature indicated that meteorological factors likely affected the inter-annual variations in the grassland growth. The peak of the grassland growth season was positively correlated with the spatial patterns of precipitation and negatively correlated with those of temperature. Precipitation during the growing season was the main influence in the arid and semi-arid regions. Monitoring grassland growth using remote sensing can accurately reveal the grassland growth status at the macro-scale in a timely manner. This research proposes an effective method for monitoring grassland growth and provides a reference for the sustainable development of grassland ecosystems.