Spring phenology is often considered the start of season (SOS) for vegetation, which can affect ecosystem photosynthesis, respiration, and evapotranspiration. However, the long-run variation of SOS remains unclear at the regional scale. In this research, the long-term variation of SOS in northern China was explored by using the updated normalized difference vegetation index and monthly climatic data during 1982–2014. Furthermore, the relative importance of climatic factors on SOS was analyzed through partial correlation and multivariate regression methods. The main results were as follows: (1) average SOS largely ranged between day 120 and 165 of the year and varied widely for different vegetation types; (2) SOS during 1982–2014 showed an advancing trend, but it appeared to be reversed after 1998; (3) preseason minimum temperature was a dominant factor controlling SOS in most pixels in northern China, followed by maximum temperature (Tmx). However, impacts of radiation and precipitation on the trend of SOS primarily depended on vegetation types; (4) impacts of climatic factors on SOS declined in the period after 1998, especially for Tmx. These findings provide important support for modeling vegetation phenology and growth in northern China.

Understanding the dynamics of vegetation autumn phenology (i.e., the end of growing season, EOS) is crucial for evaluating impacts of climate change on vegetation growth. Nevertheless, responses of the EOS to climatic factors were unclear at the regional scale. In this study, northern China was chosen for our analysis, which is a typical ecologically fragile area. Using the Enhanced Vegetation Index (EVI) and climatic data from 1982 to 2016, we extracted the EOS and analyzed its trends in northern China by using the linear least-squares regression and the Bayesian change-point detection method. Furthermore, the partial correlation analysis and multivariate regression analysis were used to determine which climatic factor was more influential on EOS. The main findings were as follows: (1) multi-year average of EOS mainly varied between 275 and 305 day of year (DOY) and had complicated spatial differences for different vegetation types; (2) the percentage of the pixel showing delaying EOS (65.50%) was larger than that showing advancing EOS (34.50%), with a significant delaying trend of 0.21 days/year at the regional scale during the study period. As for different vegetation types, their EOS trends were similar in sign but different in magnitude; (3) temperature showed a dominant role in governing EOS trends from 1982 to 2016. The increase in minimum temperature led to the delayed EOS, whereas the increase in maximum temperature reversed the EOS trends. In addition to temperature, the impacts of precipitation and radiation on EOS trends were more complex and largely depended on the vegetation types. These findings can provide a crucial support for developing vegetation dynamics models in northern China.