## 4 Discussion  
Different vegetation indices vary in their phenological performance and can provide different insights into the processes (e.g., growing season length and water conditions) that regulate ecosystem carbon exchange [35]. Time-series NDVI data from remote sensing have been widely used for studying vegetation phenology at regional, landscape, and global scales, and a number of studies used a NDVI threshold value [36; 37; 38; 30]. However, so far, few studies have evaluated the radiometric and biophysical performance of other vegetation indices, such as the EVI and LSWI from MODIS data. In this study, we used both an ecosystem-physiology approach and a remote sensing method to delineate the vegetation phenology of an alpine wetland ecosystem, and evaluated the phenology delineation performance of vegetation indices (NDVI, EVI, and LSWI) in relation to GPP. Our results clearly demonstrated that both EVI and LSWI more accurately identified the start and ending dates of plant growing season in the Zoige wetland, and hence had more biological and phenological significance in GPP predictions than the NDVI. Both EVI and LSWI provide new and complementary \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Variables**} & \multicolumn{2}{c}{**Standardized Regression Coefficients**} & \multirow{2}{*}{**Equations**} & \multirow{2}{*}{\(R^{2}\)} & \multirow{2}{*}{\(P\)} \\ \cline{2-2} \cline{5-6}  & **Ta** & & & & \\ \hline
**EVI** & 0.49 & 0.45 & GPP = \(-17.2+75.8\)Ta + 2.7 LOS\({}_{\text{EVI}}\) & 0.72 & 0.001 \\
**LSWI** & 0.45 & 0.44 & GPP = \(68.7+68.3\)Ta + 2.3 LOS\({}_{\text{ISWI}}\) & 0.68 & 0.002 \\
**GPP** & 0.58 & 0.35 & GPP = \(139.8+90.6\)Ta + 1.7 LOS\({}_{\text{GPP}}\) & 0.69 & 0.002 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The standardized regression coefficients for air temperature (Ta), the length of the growing season delineated by MODIS-based EVI (\(\text{LOS}_{\text{EVI}}\)), LSWI (\(\text{LOS}_{\text{LSWI}}\)), simulated GPP (\(\text{LOS}_{\text{GPP}}\)), and the multiple linear regression equations. GPP unit: g\(\cdot\)C\(\cdot\)m\({}^{-2}\cdot\)year\({}^{-1}\). Figure 5: The relationships between the length of growing season (LOS) delineated by EVI, LSWI, and simulated GPP by VPM and (**a**) observed annual mean Ta and (**b**) annual GPP, respectively. data to delineate land surface phenology, and these indices are more consistent with the CO\({}_{2}\) flux data than the NDVI [39]. Moreover, LSWI was more stable than NDVI and EVI for delineating the vegetation phenology of alpine wetlands (Table 1), which is consistent with recent findings from savanna ecosystems [23]. LSWI is calculated as the normalized ratio between NIR and SWIR bands [33]. The combination of NIR and SWIR bands has the potential to retrieve leaf and canopy water content (equivalent water thickness, EWT, g\(\cdot\) cm\({}^{-2}\)) and to more accurately delineate vegetation phenology [39]. The LSWI threshold value (\(\geqslant\)\(-\)0.1) has been used to determine the emergence (leaf-on) and harvest (leaf-off) of croplands [24; 34] and savanna woodlands [23]. Global climate change, especially warming, has already affected the timing of plant phenological events [40]. In this study, both the 12-year MODIS-based vegetation indices and model simulation results indicated that when the climate became warmer, the SOS advanced and the EOS delayed, which lengthened the growing season of the Zoige alpine wetland. Hence, plants interact with the seasonality of their environments, and changes in plant phenology can be regarded as sensitive indicators of climatic change [41]. A number of studies have shown that the growing season has significantly lengthened during the past decades under warmer conditions [36; 37; 38], but the connections and interactions between phenological variability and wetland C cycling are far from clear.