###### Abstract  
Satellite-based vegetation datasets enable vegetation phenology detection at large scales, among which Solar-Induced Chlorophyll Fluorescence (SIF) and Enhanced Vegetation Index (EVI) are widely used proxies for detecting phenology from photosynthesis and greenness perspectives, respectively. Recent studies have revealed the divergent performances of SIF and EVI for estimating different phenology metrics, i.e., the start of season (SOS) and the end of season (EOS); however, the underlying mechanisms are unclear. In this study, we compared the SOS and EOS of natural ecosystems derived from SIF and EVI in China and explored the underlying mechanisms by investigating the relationships between the differences of phenology derived from SIF and EVI and climatic limiting factors (i.e., temperature, water and radiation). The results showed that the differences between phenology generated using SIF and EVI were diverse in space, which had a close relationship with climatic limitations. The increasing climatic limitation index could result in larger differences in phenology from SIF and EVI for each dominant climate-limited area. The phenology extracted using SIF was more correlated with climatic limiting factors than that using EVI, especially in water-limited areas, making it the main cause of the difference in phenology from SIF and EVI. These findings highlight the impact of climatic limitation on the differences of phenology from SIF and EVI and improve our understanding of land surface phenology from greenness and photosynthesis perspectives. vegetation phenology; climatic limitation; solar-induced chlorophyll fluorescence; enhanced vegetation index 26 April 202202  
Remote sensing provides a useful approach to characterizing seasonal and interannual changes in land surface vegetation from regional to global scales [5; 6; 7]. Land surface phenology is mainly extracted based on satellite vegetation datasets using the key phenological metrics, i.e., the start of season (SOS) and the end of season (EOS) [8], to characterize the timing of vegetation dynamics during the growing season. Vegetation indices (VIs), calculated from land surface reflectance, are widely used to extract land surface phenology and analyze its response to climate change in various studies from the leaf and canopy greenness perspective. For example, the global land surface phenology product MCD12Q2 was generated by [PERSON] et al. [6; 8] using the EVI (Enhanced Vegetation Index) time series, which is the only global land surface phenology product available in recent years. However, as VIs are not capable of providing us with a direct proxy of physiological processes, they cannot be perfectly applied to modeling frameworks [9]. In this case, some studies have explored the potential of vegetation phenology extraction from an photosynthetic perspective. Solar-induced chlorophyll fluorescence (SIF), as a new physiological proxy for photosynthesis activity [10], presenting a weak signal emitted by green plants during photosynthesis [11; 12; 13; 14]. Compared to traditional VIs, SIF provides a direct indicator for monitoring vegetation physiological functioning [15; 16], and has a close relationship with carbon uptake of vegetation. Some studies have indicated that satellite-based SIF observations are highly correlated with in situ Gross Primary Productivity (GPP) over flux towers, and thus have the potential to reveal GPP dynamics under environmental changes over a large scale [15; 17]. Some studies have reported that phenology derived from SIF and EVI were different across various vegetation types [18; 19], such as coniferous forests, deciduous forests, grasslands and croplands [15; 16; 17; 20; 21]. For example, [PERSON] et al. [14] revealed that EVI-based EOS could be later than SIF-based EOS for more than two weeks in grasslands in Australia, and such differences would be larger when plants are stressed with decreasing soil moisture. Moreover, for different phenological metrics, i.e., SOS and EOS, SIF and EVI also performed differently.