## 4 Discussion  
The main purpose of this study was to evaluate the potential variables of the Sentinel-2 data using different algorithms to predict the FSV based on reliable field survey data, and to map the FSV for the first time in the southern province of China. The FSV is an important variable of forest management reports at the provincial and national levels. The use of free remote sensing imagery (e.g., Sentinel-2) and cloud processing platforms (e.g., GEE) to process and build prediction models for estimating and mapping the FSV is especially important in southern China. One of the reasons for this is that the area is covered by clouds many days each year, which seriously affects provincial forest mapping research that uses remote sensing images. Another reason is that the provinces of southern China are an important part of China's forestry. Through this research, we can effectively use the Sentinel-2 data with the GEE platform and apply the RF algorithm to spatially map the FSV in Hunan Province. In addition, this approach is also an effective way to conduct forest carbon monitoring, which is sensitive and important to climate change. Based on the spectral bands and vegetation indices extracted from Sentinel-2 data, this study has shown that B5 (Red-Edge 1) was the most important variable when estimating the FSV using both the machine learning methods and MLR method, which had been confirmed in recent studies concerning forest prediction [102] and tree species classification [103]. In the gross primary productivity field (GPP), [PERSON] et al. found that the red-edge band was useful for estimating the GPP, and noted that the red-edge reflectance was sensitive to the leaf chlorophyll content [104]. In addition, the leaf chlorophyll content was an important forest variable. Except for B5, the modeling variables selected by the machine learning models and the MLR were not the same. However, the accuracies of the different model verification results were not very different, which shows that B5 has a substantial advantage in estimating the FSV. In the study conducted by [PERSON] et al. [68], when using the RF algorithm and Sentinel-2 images to estimate the FSV in a Mediterranean forest ecosystem, they found  
Figure 9: The PALSAR-2/PALSAR Forest/Non-Forest Map in 2017 (left), and the predicted FSV map in 2017 in Hunan Province (right). that the most important variable was B11 (SWIR 1), which was different from our findings in this study. Our research was consistent with a study conducted by [PERSON] et al. [4], which showed that B5 was the most important variable with which to predict the FSV. Regarding the FSV prediction performance, we compared our \(R^{2}\) that measured our predictive capability with that of [PERSON] et al. [4], who conducted research using Sentinel-2 and multilayer perceptron and regression trees to estimate the FSV; we found that our results (\(R^{2}\) = 0.58) were slightly better than their best results with a multilayer perceptron model (\(R^{2}\) = 0.56). This observation may occur because the RF algorithm usually performs better than the multilayer perceptron [71], or the sample survey data based on our advanced positioning technology improved the estimation accuracy. Regarding the variable selection in machine methods, our results also showed that the traditional vegetation indices did not perform well when estimating the FSV in this study, and [PERSON] et al. [105] found the same trend, i.e., that the original band performed better than vegetation indices when estimating the FSV. Table 6 shows that the training and test results before and after selecting the variables of the RF modeling were basically the same, which indicated that the VSURF package was a good tool with which to select variables for RF modeling. However, Table 6 also shows that the selected variables had a certain impact on SVR modeling. This is because the VSURF package is a variable selecting tool based on the RF model, and is not fully applicable to the SVR model.