## Materials and Methods
### Generation of Land Use and Land Cover Datasets  
Landsat TM/ETM images in 2000 and 2005, and HJ-1 A/B images in 2010 with a 30 m spatial resolution were used to interpret LULC change. Using the FAO Land Cover Classification System (FAO LCCS) tools [38], we classified the images into 38 LULC level II classes [39], and then categorized them into six LULC level I classes, which were forest, grassland, wetland, cropland, artificial land, and other types land, based on the IPCC classification system [40].  
We derived the 2010 LULC vector dataset from 2010 HJ-1 A/B images by applying object-oriented automatic classification methods after data preprocessing on a supercomputing platform, with associated data from ground surveys and radar [41]. The overall producer's accuracy for the 2010 LULC dataset was validated with a total of 31,675 independent ground survey samples based on random sampling and reached 94% and 86% accuracy on average for LULC classes at level I and level II, respectively.  
The 2000 and 2005 LULC vector datasets were generated using object-oriented spectrum eigenvectors similarity change detection. The spectrum eigenvectors of a Landsat TM image patch can be regarded as a vector in a six-dimensional feature space, and the smaller the included angle and closer the norm of two vectors, the more similar two patches are. Based on this principle, we used the cosine of the angle between two vectors and the ratio of the vector norm to measure vector similarity, see [PERSON] & [PERSON] [42] for details. In eCognition Developer 8.64, we used the 2010 LULC vector dataset  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Assessment Categories** & **Assessment Content** & **Assessment Indicators** & **Notes** \\ \hline \multirow{4}{*}{Ecosystem pattern} & Ecosystem composition & \multirow{2}{*}{Ecosystem area} & \multirow{2}{*}{Ecosystem} \\  & and distribution & & \\ \cline{2-3}  & Ecosystem & & \\  & transformation & & \\  & characteristics & & \\ \hline \multirow{4}{*}{Ecosystem quality (EQ)} & Fractional vegetation & Annual maximum value & \\  & cover (FVC) & of FVC (FVC\({}_{\text{max}}\)) & \\ \cline{2-3}  & Leaf area index (LAI) & Annual maximum value & \\  & & of LAI (LAI\({}_{\text{max}}\)) & Dimensionless variable \\ \cline{2-3}  & Net primary & Annual average value of & \\  & productivity (NPP) & NPP (NPPavg) & \\ \hline \multirow{4}{*}{Key ecosystem services (ES)} & Water regulation (WR) & Annual quantity of water & \\  & & regulation per unit area & \\  & & (WR\({}_{\text{pua}}\)) & \\ \cline{2-3}  & Soil conservation (SC) & Annual quantity of soil & Contrary indicator, lower & \\ \cline{2-3}  & Soil conservation (SC) & water loss per unit area & values are better \\ \cline{2-3}  & Sandstorm prevention & Annual quantity of soil & \\  & (SP) & wind loss per unit area & Contrary indicator, lower & \\  & & (SWIL\({}_{\text{pua}}\)) & \\ \hline \hline \end{tabular}
\end{table}
Table 2: The comprehensive assessment indicator system for ecological changes of the Grain for Green Program (GGP).  
generated above to overlap and segment the 2000 and 2005 Landsat TM images with 2010 HJ-1 images at multiple scales, which ensured that the unchanged areas had the same border and provided a priori information on ground objects, enhancing the classification accuracy. We extracted spectrum eigenvectors from each image patch segmented above and calculated the similarity between the images of target years (2000 and 2005) and the base year (2010) to detect LULC change, and then updated the LULC dataset accordingly [43]. A detailed workflow for the LULC datasets in the target years is displayed in Figure 2.