## 2 Method and Material
### Evaluation of the Propagation of Bias in Phenology  
The onset and offset dates derived using different methods with satellite data in Section 2.3 were used to parameterize an onset model [42] (referred to as the \"Sequential\" model hereafter) and an offset model [43] (referred to as the \"Delpierre\" model hereafter). The \"Sequential\" model assumes that leaf onset is triggered when a critical GDD threshold is exceeded after a chilling requirement is fulfilled (Equation (4)). The \"Delpierre\" model assumes that both temperature and photoperiod control the senescence process (Equation (5)). We chose the \"Delpierre\" model because it has been proven to have relatively good performance [13]. Although several model structures are available for the onset, we only use the \"Sequential\" model, which has moderate complexity in terms of parameter number, as an example to show the propagation of bias in phenology. Tests of the \"Spring Warming\" [44] and \"Parallel\" models [45] did not change our conclusion and results are not shown. _Remote Sens._**2014**, \(6\)  
\[\left\{ \begin{array}{l}S_{c}=\sum_{t_{0}}^{t_{h}}R_{c}(x_{t})\text{ if }T_{ chill}>x_{t}\text{ then }R_{c}=1\text{ else }R_{c}=0\\ \text{ When }S_{c}\geq C_{total}\text{ heat accumulation starts}\\ S_{f}=\sum_{t_{h}}^{t_{h}}R_{f}(x_{t})\text{ if }T_{base}\geq x_{t}\text{ then }R_{f}=0\text{ else }R_{c}=x_{t}-T_{base}\\ \text{ When }S_{f}\geq F^{*}\text{ onset is triggered}\end{array} \right. \tag{4}\]  
\[\left\{ \begin{array}{l}\text{ If }P(t)\leq P_{start}\text{ and }x_{t}\leq T_{ chill}\text{ then }S_{off}=\sum R_{off}(x_{t})\\ \text{ Where }R_{off}(x_{t})=[T_{chill}-x_{x}]^{a}\times[P(t)/P_{start}]^{b}\\ \text{ When }S_{off}\geq Y_{crit}\text{ leaf offset is triggered}\end{array} \right. \tag{5}\]  
where \(x_{t}\) is the temperature at time \(t\); \(R_{c}\) is the rate of chilling (day); \(T_{chill}\) is base temperature (\({}^{\circ}\)C) required by chilling accumulation process; \(S_{c}\) is the accumulated chilling units (day); \(C_{total}\) is the critical threshold of the chilling process (day); \(R_{f}\) is the rate of heat forcing (degree-day); \(T_{base}\) is the base temperature (\({}^{\circ}\)C) required by the heat accumulation process; \(S_{f}\) is the accumulated heat forcing units (degree-day); \(t_{0}\) is the starting date of accumulation (DOY); \(t_{h}\) is the date when the chilling accumulation is completed (DOY); \(t_{b}\) is the date of onset (DOY); \(F^{*}\) is the critical threshold of heating process (degree-day); \(P(t)\) is the photoperiod for day \(t\) (hour); \(P_{start}\) is the photoperiod threshold for offset process (hour); \(R_{off}\) is the rate of forcing for offset process (\({}^{\circ}\)C hour hour\({}^{-1}\) day\({}^{-1}\)); \(S_{off}\) is the accumulated forcing units for offset (\({}^{\circ}\)C hour hour\({}^{-1}\)); \(a\) and \(b\) are parameters of the \"Delpierre\" model; and \(Y_{crit}\) is the critical threshold of the offset process (\({}^{\circ}\)C hour hour\({}^{-1}\)). Data used to drive the phenology models include daily temperature (taken here from the ZedX dataset) and photoperiod, which is calculated as a function of latitude and DOY [46]. A simple genetic algorithm written in Interactive Data Language [47] was applied to optimize the model parameters by minimizing the RMSD between the modeled and remotely sensed phenological dates. Convergence was achieved when RMSD could no longer be reduced or 100 generations of parameters were reached. All the parameters of the phenological models were optimized (_i.e._, \(T_{chill}\), \(C_{total}\), \(T_{base}\), and \(F^{*}\) for the \"Sequential\" model; \(P_{start}\), \(T_{chill}\), \(a\), \(b\), and \(Y_{crit}\) for the \"Delpierre\" model). Data from the period of 2000-2007, which is the overlap between the references (_i.e._, remotely sensed phenology available since 2000) and the driving data (_i.e._, ZedX data, available for 1948-2007), were used for the optimization.