## 3 Methods  
### Procedure for Grassland AGB Estimation  
The development of the AGB estimation model took place via four main steps (Figure 2).  
Step I: Generation of synthetic NDVI by using STARFM. We obtained the synthetic NDVI for the AGB estimation model based on STARFM with the Landsat spatial resolution and MODIS temporal frequency. The base data pairs are shown in Table 1. The STARFM algorithm is described in more details in Section 3.2.  
Step II: Development of the AGB estimation model. A total of 68 field samples were collected during June to August in 2011, and these data were used for the development and validation of the AGB model. We used the training set (46 samples) to construct four AGB estimation models, including linear regression model, power function model, exponential model, and SVM model. We then used the remaining of 22 plots (1/3 of the samples) as the testing data set to evaluate the four AGB estimation models.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Year** & **Landsat TM Path = 124, Row = 29/30** & **MODIS (DOY) Tile = h26v04** \\ \hline
2005 & 09/02/2005 & 241/2005 \\
2006 & 09/21/2006 & 265/2006 \\
2007 & 09/08/2007 & 249/2007 \\
**2008** & **09/08/2007** & **249/2007** \\
2009 & 08/12/2009 & 225/2009 \\
2010 & 08/31/2010 & 241/2010 \\
2011 & 08/02/2011 & 209/2011 \\
**2012** & **08/02/2011** & **209/2011** \\
**2013** & **08/02/2011** & **209/2011** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) image pairs for generating synthetic Normalized Difference Vegetation Index (NDVI) data during 2005–2013. Years highlighted with bold font lacked Landsat images, and therefore, the Landsat images from adjacent year (1-yr interval) or 2-yr intervals were used.  
Step III: Choose the optimal model for biomass estimation. To evaluate the model's performance, we used three statistical indices, namely, the coefficient of determination (\(R^{2}\)), root-mean-square error (\(RMSE\)), and relative RMSE (\(RMSE_{r}\)) [52]. The RMSE and \(RMSE_{r}\) were calculated as follows:  
\[RMSE\ =\ \sqrt{\sum_{i\ =\ 1}^{n}\left(y_{i}-\hat{y}_{i}\right)^{2}/n} \tag{1}\]  
Figure 2: Procedures for developing the aboveground biomass (AGB) estimation model. Pre_NDVI represents the synthetic NDVI series.  
\[RMSE_{r}\ =\ \left(\sqrt{\sum_{i\ =\ 1}^{n}\left(y_{i}-\hat{y_{i}}\right)^{2}/n /\overline{y_{i}}}\right)\times 100\% \tag{2}\]  
where \(y_{i}\) is the observed value, \(\hat{y_{i}}\) is the model predicted value, \(\overline{\hat{y_{i}}}\) is the mean of the modeled values, and n is the sample number. In this study, we found that the SVM (see Section 3.3) was the optimal model for biomass estimation in this region.  
Step IV: The Standardized Anomalies Index (SAI) [53] was used to detect the spatial and temporal distribution of biomass anomalies in July 2007. The SAI is defined as follows:  
\[SAI_{AGB}=\ (AGB_{i}-\overline{AGB})/\sigma_{AGB} \tag{3}\]  
Here, \(SAI_{AGB}\) represents the AGB anomalies, \(AGB_{i}\) is the annual AGB in the \(ith\) year, \(\overline{AGB}\) is the mean AGB for the years of 2005-2013, and \(\sigma_{AGB}\) is the standard deviation of the 9-yr AGB. Positive SAI values indicate that the annual AGB is larger than the 9-yr mean, while negative SAI values indicate that the annual AGB is more deficient compared to the 9-yr mean. In this study, we compared the \(SAI_{AGB}\) generated from the synthetic NDVI-derived biomass images and from the MODIS NDVI-derived biomass images to detect whether the synthetic images can help capture more details of the biomass anomalies in Xilinhot.