## 2 Materials and Methods
### Data Acquisition and Processing
#### 2.3.2 Bamboo Forest Distribution Data of China  
The distribution information of Chinese bamboo forests in 2003, 2008, 2014, and 2018 was extracted. The information on Chinese bamboo forests from 2003, 2008, and 2014 has been extracted in our previous study [42]. On this basis, we extracted information on Chinese bamboo forests from 2018. The flow chart of bamboo extraction is shown in Figure 2.  
The main process is as follows: First, a total of 23 multi-temporal MODIS NDVI images are available. In order to further improve the MODIS NDVI data quality, these 23 images were composited into 12 multi-temporal images by selecting a maximum of two corresponding pixels of two neighboring MODIS NDVI images as the value of a new pixel (\(\rm NDVI_{max12}\)) [55]. Then, a minimum noise fraction (MNF) transform [56] was employed to convert the \(\rm NDVI_{max12}\) data to obtain the principal component variables of \(\rm NDVI_{max12}\) data (\(\rm NDVI_{max12}\) MNF), and the first six bands with a cumulative contribution rate greater than 90% (\(\rm NDVI_{max12}\) MNF\({}_{1.6}\)) were retained for classification. Second, according to the image texture and spectral information features, the five types of samples (forest, farmland, water, bare land, and residential land) were selected by visual interpretation [57], and then the study area was classified by the maximum likelihood classification (MLC). On this basis, the forest information in China to extract the normalized difference vegetation of forests (\(\rm NDVI_{forest\_12}\)) and the land surface reflectance of forests (\(\rm REF_{forest\_7}\)), MNF was then performed on them to obtain the principal component variables of \(\rm NDVI_{forest\_12}\) data (\(\rm NDVI_{forest\_12}\) MNF) and \(\rm REF_{forest\_7}\) data (\(\rm REF_{forest\_7}\) MNF). We retained the bands with a cumulative contribution rate greater than 85%, that is, the first nine bands of \(\rm NDVI_{forest\_12}\) MNF data (\(\rm NDVI_{forest\_12}\) MNF\({}_{1.9}\)) and the first five bands of \(\rm REF_{forest\_7}\) MNF data (\(\rm REF_{forest\_7}\) MNF\({}_{1.5}\)). On this basis, according to the training samples of bamboo forests, broad-leaved forests, and coniferous forests, the corresponding attribute values were extracted as the characteristic variables to construct a decision tree model, and the information on Chinese bamboo forests was extracted by using the constructed decision tree model (Figure 3). In this study, 85 bamboo forest survey samples from Zhejiang Province in 2019 and 440 bamboo forest samples from China selected  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**MODIS** & **Abbreviation** & **Time** & **Spatial Resolution** & **Time Resolution** & **To Use** \\ \hline MOD13A2 & NDVI & 2018 & 1000 m & 16 days & Extract the bamboo forest \\ MOD09A1 & REF & 2018 & 500 m & 8 days & Extract the bamboo forest \\ MOD15A2 & LAI & 2001–2018 & 1000 m & 8 days & Model input \\ \hline \end{tabular}
\end{table}
Table 1: MODIS data and descriptions.  
from Landsat 8 images in 2018 were used as bamboo forest verification samples for point-by-point verification. Finally, the least-squares mixed-pixel decomposition method [58] was used to obtain the abundance information of bamboo forests in China. The results were presented in Figure 1a.  
Figure 2: Flow chart of bamboo extraction.  
It was verified that the accuracy of bamboo forest extraction was 76.54-81.56%, and that the extracted area was close to the inventory area of forest resources (Table 2), which laid the foundation for the simulation of GPP and NPP for bamboo forests in China. Only the information of bamboo forests in 2003, 2008, 2014, and 2018 was extracted; therefore, the bamboo forest information of the unclassified year was replaced by a similar year from which the bamboo forest information was extracted.