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Autumn vegetation phenology plays a critical role in identifying the end of the growing season and its response to climate change. Using the six vegetation indices retrieved from moderate resolution imaging spectroradiometer data, we extracted an end date of the growing season (EOS) in the temperate deciduous broadleaf forest (TDBF) area of China. Then, we validated EOS with the ground-observed leaf fall date (LF) of dominant tree species at 27 sites and selected the best vegetation index. Moreover, we analyzed the spatial pattern of EOS based on the best vegetation index and its dependency on geo-location indicators and seasonal temperature/precipitation. Results show that the plant senescence reflectance index-based EOS agrees most closely with LF. Multi-year averaged EOS display latitudinal, longitudinal and altitudinal gradients. The altitudinal sensitivity of EOS became weaker from 2000 to 2012. Temperature-based spatial phenology modeling indicated that a 1 K spatial shift in seasonal mean temperature can cause a spatial shift of 2.4-3.6 days in EOS. The models explain between 54% and 73% of the variance in the EOS timing. However, the influence of seasonal precipitation on spatial variations of EOS was much weaker. Thus, spatial temperature variation controls the spatial patterns of EOS in TDBF of China, and future temperature increase might lead to more uniform autumn phenology across elevations.