## 1 Introduction  
Vegetation phenology characterizes timing of leaf development, senescence and abscission. It is highly sensitive to changes in climate conditions, especially air temperature [1; 2; 3]. Many studies have shown advancement of leaf-out and longer growing season in the recent half-century [4; 5]. Changes in vegetation phenology may in turn alter land ecosystem productivity and seasonal variations in CO\({}_{2}\) fluxes and the energy balance [6; 7]. Phenology metrics such as start-of-season (SOS) and end-of-season (EOS) can be derived from either satellite or near surface remote sensing observations. For example, data from MODerate resolution Imaging Spectroradiometer (MODIS) have been utilized to monitor phenology of different ecosystems across the globe [8; 9; 10]. Compared to satellite-derived phenology that may contain large heterogeneity within each pixel, near surface remote sensing usually has much smaller footprints and thus can provide more species-centered phenology estimation [11; 12]. The PhenoCam network ([[http://phenocam.sr.unh.edu/](http://phenocam.sr.unh.edu/)]([http://phenocam.sr.unh.edu/](http://phenocam.sr.unh.edu/))) with webcams at more than 300 sites has been developed to monitor phenology [13; 14] at a small ecosystem scale. Optimal color indices, such as the greenness index, estimated from the PhenoCam network, have been used to characterize phenology of various ecosystems [13; 15]. Specifically, phenological transitions such as SOS and EOS are determined from selected time series by detecting inflection points in fitted curve. Usually, logistic functions [13] are used to fit time series observations. If both visible and infrared band data are available, the Normalized Difference Vegetation Index (NDVI) [16], a spectral vegetation index (VI), is most widely used to characterize the seasonal dynamics of vegetation. Both field measured NDVI and satellite remotely sensed NDVI have shown their potential to monitor phenology at different spatial scales. For example, [PERSON] et al. [17] derived land surface phenology from NDVI time series provided by MODIS. [PERSON] et al. [18] used ground-based NDVI time series to assess the impacts of structural and biochemical changes on the phenological patterns. Although much progress has been achieved in monitoring/modelling phenology, recent studies show that discrepancies still exist between plant photosynthetic activities and phenological development derived from reflectance-based data [19; 20]. Specifically, reflectance-based data may result in both earlier spring onset and later leaf drop such that reflectance-based growing season is longer than the one derived from the GPP estimates. This dissimilarity could be explained by many factors including difference in spatial scales [21], temporal resolutions [22] and understory contribution [23]; in particular, the disengagement between greenness and photosynthesis processes in deciduous forests could be explained by the phenomenon that leaves emerge before start of photosynthetic activity in spring and leaf coloration lags behind the end of carbon assimilation in fall [20; 21; 22; 23; 24]. In addition, boreal evergreen forests were found to have large-scale decoupling of photosynthesis and greenness [25]. It suggests that reflected signals used by many phenology detection methods lack an inherent link to plant photosynthetic activity [26; 27] and thus a more direct proxy for photosynthetic activity is needed. More recently, solar-induced chlorophyll fluorescence (SIF) emission is found to have close connection with plant photosynthetic activities [28; 29; 30]. SIF is a by-product of the photosynthesis process: a small fraction of absorbed Photosynthetically Active Radiation (APAR) is remitted at a longer wavelength (640-850 nm).