## 1 Introduction  
Vegetation phenology, or the timing of plant growth stages (e.g., the timing of budburst, flowering, leaf coloring), is considered a robust indicator of short-term climate variation and long-term climate trends because it is driven by environmental factors, such as temperature, precipitation and photoperiod. Vegetation phenology has received increased attention recently because evidence from ground observations as well as satellite remote sensing has shown that vegetation phenology has shifted during the past few decades [1; 2; 3; 4; 5], especially at middle and high latitudes of the Northern Hemisphere, as a result of increasing average temperature [6; 7]. On the other hand, shifts in vegetation phenology can exert strong control on the feedbacks between the biosphere and atmosphere by affecting biogeochemical processes (e.g., exchange of carbon dioxide, production of biogenic volatile organic compounds) and biophysical properties (e.g., seasonal variation in albedo) of ecosystems [8; 9]. Bias in vegetation phenology therefore may lead to errors in carbon and water exchange and energy budgets simulated in dynamic ecosystem models (DEMs) [10] as well as climate patterns simulated in coupled global climate models (GCMs) [11]. A multi-model synthesis study has shown that vegetation phenology is poorly represented in many terrestrial biosphere models [10], which highlighted the urgency of improving phenological models embedded in DEMs. Phenological models can potentially be improved by reducing the uncertainties that stem from model structure, model parameters, or drivers [12]. For example, a comprehensive comparison of existing phenological models across geographic zones may help reduce the structural uncertainties [13]. Moreover, modeling studies at the regional scale demonstrated that, due to the difference in species type and composition, forests at different locations do not share common parameters, such as base temperature for growing degree day (GDD) calculation [13; 14]. Thus, location-specific parameterization has the potential to reduce the uncertainty associated with model parameters. Parameterization of phenological models at a specific location requires corresponding phenological observations. As ground-based phenological observations are limited in spatial coverage and quantity, phenology derived from remote sensing becomes the only alternative when parameterization over a large continuous area is needed. Phenology derived from remote sensing, which has recently been referred to as land surface phenology (LSP) in order to distinguish it from _in situ_ monitoring at species level, has long been used to examine phenological changes [2; 4; 5; 15; 16; 17] and to develop large-scale phenology models [18; 19]. Numerous remote sensing methods, such as vegetation index threshold and curve fitting, have been developed to extract phenological metrics that describe particular timing related to leaf behaviors and photosynthetic activities [19; 20; 21; 22; 23; 24; 25]. Start of season (SOS) and end of season (EOS) [22; 26], or onset and offset [19] are two phenological phases (_i.e_., phenphases) most commonly extracted due to their importance in determining the growing season length (GSL). Some studies also derive more than two phenphases. For example, [PERSON] _et al_. extracted four phenphases including the onset of greenup, maturity, senescence and dormancy [25]. Most of the methods used to extract phenological metrics are based purely on time series of the normalized difference vegetation index (NDVI) [19, 23] and enhanced vegetation index (EVI) [22, 25] from various sensors (e.g., Advanced Very High Resolution Radiometer (AVHRR), Moderate-Resolution Imaging Spectroradiometer (MODIS)). Although phenological metrics derived using different methods share the same name (e.g., SOS), they could actually represent different phenological stages (e.g., the timing when vegetation starts to green up, the timing when vegetation grows the fastest).