**4. Discussion**

In this study, we analyzed the characteristics of phenology derived from SIF and EVI for natural vegetated areas in China and found substantial differences between SOS/EOS generated using SIF and EVI. Specifically, the SOS derived from SIF was generally later than that derived from EVI, which was the case in 70% of the total natural vegetated area in China. We found this occurred in climatic limiting areas, where deciduous forests, mixed forests and grasslands were mainly distributed. Those vegetation types initiate photosynthesis after green leaves emerge in spring [17,20]; thus, photosynthesis phenology tends to be later than greenness phenology for SOS, which explains our results. In those areas covered by evergreen forests in the south with no distinct climatic limitations, the SOS derived from SIF was slightly earlier than that from EVI. A higher PAR supply in humid areas would stimulate photosynthesis more quickly, leading to photosynthesis starting earlier than greenness in spring for evergreen forests [15,19,36]. The EOS from SIF was generally earlier than that from EVI, which is consistent with previous studies [15,17,19], implying seasonal hysteresis of EVI in response to photoperiod changes in the period of senescence [15,17].

Furthermore, we revealed that the differences between phenology generated using SIF and EVI were diverse in SOS and EOS. We found that the difference in EOS generated using SIF and EVI was generally larger than that generated using SOS. Possible reasons include the following: (a) The autumn phenology extracted from satellite VIs had higher uncertainty (and perhaps bias) relative to spring phenology [37]. For example, Lu et al. [20] presented that EVI could hardly predict the autumn phenology of deciduous forests accurately with an overall R<sup>2</sup> less than 0.3, while the R<sup>2</sup> of spring phenology was generally higher than 0.7. (b) Seasonal decoupling of physiological status and greenness information occurred in autumn. Specifically, SOS derived from SIF and EVI occur relatively synchronously, but they become increasingly asynchronous as the growing season progresses [38], leading to larger differences in EOS generated using SIF and EVI than that in SOS.

We further inferred that the differences between SIF-based phenology and EVI-based phenology in space have a close relationship with their different responses to climatic limitations. In contrast to information about green biomass proxied by EVI, SIF contains information on the absorbed photosynthetically active radiation by vegetation (APAR) and environmental stresses (especially water stress) related to photosynthetic light-use efficiency (LUE) [10]. Therefore, SIF is more sensitive to climate variability than EVI [39,40]. This is consistent with our finding that phenology from SIF was more correlated with climatic limitations than that from EVI, making it the main cause of the difference between phenology generated using SIF and EVI. Under these divergent responses to climatic limitations, the differences in SOS and EOS from SIF and EVI become larger, along with a higher climatic limitation index. However, in the radiation-limited area, a higher radiation limitation index did not contribute to a larger difference in EOS derived from SIF and EVI. This happened as EOS derived from SIF and EVI had similar regression slopes with the radiation limitation index, suggesting that autumn phenology is more radiation-limited than spring phenology from both greenness and photosynthesis perspectives [41]. In

addition, the radiation-limiting area in this study was distributed in northern China, where snow cover existed in autumn and winter, which may introduce the undesired errors of EOS extracting from reflectance-based EVI [42].

Although the GOSIF product was generated using remote sensing data from the MODIS and meteorological reanalysis data as inputs to the predictive SIF model, which may increase the correlation of SIF and climatic factors in a time series, this correlation from data sources will be offset in the spatial statistics adopted in this study. In addition, we employed SIF and EVI to extract phenology from photosynthesis and greenness perspectives, respectively. Other proxies, such as Chlorophyll/Carotenoid Index [43], Normalized Difference Vegetation Index, can be further analyzed in future studies to investigate the unique characteristics of each proxy on remote sensing derived phenology. The relationship between vegetation phenology and multiple climatic factors instead of one dominant climatic limitation index needs to be analyzed further to explore whether and how the impacts of climatic interactions on vegetation dynamics. Moreover, we focused on natural vegetated areas in China as a target, as it provides a natural laboratory with a wide variation of ecosystems and climate types. Further research could be expanded to the hemisphere or global scale to evaluate our findings.
