## 2 Preparation
### Data
#### 2.2.3 LAI Time Series Data  
*MODIS-based LAI time series data  
The MODIS reflectance products (i.e., MOD09A1) with 500 m spatial resolution were downloaded from MODIS data center ([[https://lpdacc.usgs.gov](https://lpdacc.usgs.gov)]([https://lpdacc.usgs.gov](https://lpdacc.usgs.gov))). The MODIS reprojection tool (MRT) was first used to extract red, near infrared, shortwave infrared, solar zenith angle, sensor zenith angle, and the relative azimuthal angle between the sun and sensor information from MOD09A1 data. After converting all maps' projection into the Universal Transverse Mercator (UTM) projection, we produced the LAI maps with eight-day interval from 2009 to 2012 using the 4-scale geometric optical model by following the method developed by [PERSON] et al. [30]. Then the locally adjusted cubic-spline capping (LACC) algorithm [31] was employed to get the final LAI seasonal curve by removing the outliers.
* Landsat-based LAI time series data  
To produce high resolution LAI time series, three Landsat ETM+ images (19 August 2008, 30 August 2012, 2 September 2013) and three Landsat TM images (30 August 2009, 2 September 2010, 5 September 2011) for the Hulun Buir region (path: 122 and row: 25) acquired during the growing season were downloaded from the EROS Data Center ([[http://earthexplorer.usgs.gov/](http://earthexplorer.usgs.gov/)]([http://earthexplorer.usgs.gov/](http://earthexplorer.usgs.gov/))). Because of the gaps resulting from the scan line corrector breakdown of Landsat ETM+ after May in 2003, we first filled the image gaps using the geo-statistical neighborhood similar pixel interpolator (GNSPI) algorithm based on the spectral information of spatial neighboring pixels [32]. We also conducted geometric and radiative corrections for all Landsat TM/ETM+ images to remove geometric distortions and atmospheric effects [33; 34],respectively. In addition, to reduce the atmospheric environment differences among all the Landsat TM/ETM+ images, we conducted the radiative normalization process for these images using the pseudo invariant features (PIFs) (i.e., buildings, roads, or deep water etc.).  
After getting all the needed Landsat TM/ETM+images, we first generated the Landsat-based LAI map using the statistical model between the field-based LAI and vegetation index for 2012 and 2013. Based on the MODIS-based LAI seasonal curves and the Landsat-based LAI map for 2012, the eight-day interval time series Landsat-based LAI maps from 2009 to 2012 were produced using the method developed by [PERSON] et al. [35]:  
\[T_{\text{LAI,i}}=M_{\text{LAI,i}}+\frac{46-D_{\text{i}}}{46}\times d \tag{1}\]  
where \(T_{\text{LAI,i}}\) is the LAI value (m\({}^{2}\)/m\({}^{2}\)) for ith day of a year with 30 m spatial resolution; \(M_{\text{LAI,i}}\) is the LAI value (m\({}^{2}\)/m\({}^{2}\)) for ith day with 500 m spatial resolution for the same forest type corresponding to \(T_{\text{LAI,i}}\); \(D_{i}\) is the date difference (day) between ith day and the nearest day of TM/ETM+-LAI value; \(d\) is the LAI value (m\({}^{2}\)/m\({}^{2}\)) difference between the predicted date of Landsat TM/ETM+ and the same date of MODIS-LAI. Additionally, the Landsat image of 2008 was also used to produce the baseline AGB to drive the BEPS model.