The end of growing season (EOS) is an effective indicator of annual vegetation growth. Previous studies have revealed the dynamics of the EOS with climate change, while the influence of vegetation growth in preceding stage and peak of growing season (POS) on the EOS has not been thoroughly documented. In this study, we used four smoothing methods to obtain EOS dates from the Normalized Difference Vegetation Index (NDVI) in northeast Inner Mongolia (NIM) between 2001-2017, assessed the differences in the spatiotemporal variations of the EOS obtained by the four smoothing methods, and then investigated the impacts of climate factors, summer/ autumn vegetation growth and POS on the EOS. The results showed that the EOS dates obtained with different smoothing methods were broadly consistent in terms of their spatial patterns and temporal trends. In terms of climate factors, the EOS was driven mainly by preseason precipitation for the majority of vegetation types and advanced with increasing precipitation. For the steppe, both minimum temperature (Tmin) and relative humidity (RHU) played the most important roles in regulating the variation of EOS which was delayed with an increase in Tmin and reduction in RHU. Furthermore, our study found an earlier POS and vigorous vegetation growth in summer would jointly advance the steppe EOS, but these relationships were the opposite of each other in meadow and forest regions. Interestingly, the EOS of NIM was more related with vegetation growth in the most recent period before the EOS. This study highlights the importance of ecological processes in the preceding growth stage for understanding the dynamics of EOS. INDEX TERMSEnd of growing season, Northeast Inner Mongolia, Climate change, Peak of growing season, Preceding growth stage of vegetation This work is licensed under a Creative Commons Attribution 4.

Accurate estimation of gross primary productivity (GPP) from the regional to global scale is essential in modeling carbon cycle processes. The recently-developed two-leaf light use efficiency (TL-LUE) model and its revised versions based on different concepts have significantly improved the underlying mechanisms between model assumptions and photosynthetic processing. Yet few studies have compared the advantages of the various two-leaf LUE models for their practical applications. Here, an integrated model referred to as a three-parameter radiation-constrained mountain TL-LUE (RMTL3-LUE), is proposed by combining the radiation scalar of the [radiationconstrained TL-LUE model] and the topographic parameters of the [mountainous TL-LUE model]. In this way, the importance of light intensity and topography on vegetation photosynthesis is integrated. Our calibration and validation of RMTL3-LUE were carried out for 11 ecosystems with in situ eddy covariance measurements around the globe. This indicates that the model can effectively improve the GPP estimates compared to its predecessors. At the landscape scale, RMTL3-LUE can also realistically quantify topographic effects on photosynthesis, with topographic sensitivities of decreasing (increasing) with the slope on the unshaded (shaded) terrain. Furthermore, RMTL3-LUE displays an asymmetric sensitivity to PAR variability, with a low sensitivity to PAR compared to other models under high PAR conditions and a similar sensitivity to PAR in low PARs.Altogether, it is clear that the integration of the merits of multiple TL-LUE models can further improve the photosynthetic processes for various conditions amid more challenges in constructing more complex models.

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