Abstract
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.
Highlights
Tracking the vegetation growing season is crucial for assessing an ecosystem response to climate change [1] and its impacts on carbon, water and energy exchange between vegetation and the atmosphere [2,3,4,5]
Results show that the medians and ranges of RMSE for end date of the growing season (EOS) retrieved from all six vegetation indices (VIs) were between 14 days and 23.2 days, and between 5.6 days and 11 days, respectively, in which either the median or range of RMSE for Plant Senescence Reflectance Index (PSRI)-derived EOS was the smallest (Figure 3)
EOS dates retrieved from all six VIs were significantly (p < 0.001) and positively correlated with leaf fall date (LF) dates, the satellite estimates were systematically earlier (Figure 4)
Summary
Tracking the vegetation growing season is crucial for assessing an ecosystem response to climate change [1] and its impacts on carbon, water and energy exchange between vegetation and the atmosphere [2,3,4,5]. To reveal climate drivers of phenological spatial patterns, Chen and Xu [5] analyzed relationships between the leaf fall end date of Ulmus pumila trees and the preseason temperature across geographic locations in China’s temperate zone, and found that the spatial differences of preseason temperature control the spatial differences of Ulmus pumila leaf fall date. Whether this kind of spatial association based on an individual species is applicable to the ecosystem level is still unclear. Since spatial patterns of phenology reflect an adaptation of plant life cycle stages to different climates [17], modeling the spatial pattern and its interannual variation in autumn phenology will facilitate a better understanding of phenological responses to recent climate change
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
