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Plant phenology is of great significance for global change study and it serves as an important indicator of vegetation productivity. Increasing efforts have been made to retrieve the plant phenology using remote sensing observations at regional-to-global scale due to its large spatial coverage. Compared with our understanding on drivers of spring phenology, it remains unclear that to which extent is leaf coloration in autumn controlled by climate forcing, especially on the relative importance between daytime and nighttime temperatures. Using a total of 160 site-year leaf coloring date (LCD) data observed from 14 sites in China, we showed that three frequently used remote sensing algorithms (i.e., the dynamic threshold approach, the simple and double logistic approaches) were not able to accurately retrieve LCD. Surprisingly, the nighttime land surface temperature (LSTnight) explained as much as 62% of LCD variability, compared with 28% for daytime temperature (LSTday). We, therefore proposed a new model that combines the enhanced vegetation index and LSTnight for the reconstruction of LCD. We demonstrated that LCD of China's ecosystems has been delayed at a rate of 0.7 days per year over 2003-2014, and a longer LCD contributed to the increased annual gross primary productivity for most (66%) regions. Our results have important implications as it sheds light on the role of LSTnight in controlling plant phenology. This article strongly suggests the combined use of vegetation index and LSTnight in the reconstruction of phenological variations in autumn across species and plant functional types.

Satellite Observed Strong Relationship Between Nighttime Surface Temperature and Leaf Coloring Dates of Terrestrial Ecosystems in East China