## 4 Discussion
At the end of 2017, the Hunan provincial government published a report on the FSV stating that the total value was \(5.48\times 10^{8}\) m\({}^{3}\)[107]. Based on this observation, the RF model reached an accuracy of 63.87% in predicting the FSV. We also focused on the forest area reported on the government report in 2017; this showed that the forest area was \(1.3\times 10^{7}\) ha [107], which was different from that extracted from the PALSAR-2/PALSAR Forest/Non-Forest product in 2017 (\(8.95\times 10^{6}\) ha). This difference is also an important factor that affects the estimation result of the FSV. In addition, we paid attention to the study conducted by [PERSON] et al. [83], who predicted the forest biomass in Guangdong Province using the RF model and remote sensing data, which achieved slightly lower accuracy (58.88%) than that observed in this study. However, one important difference is that [PERSON] et al. estimated the biomass, and we estimated the FSV. Since Hunan Province is located in southern China, the greatest challenge was to obtain images with little or no cloud data that covered the whole province. To reduce the effects of clouds and noise on the images, we used three masking methods. To reduce or even eliminate the holes caused by the masks on the images, we processed a total of 3150 images and extracted the median values of the overlapping image parts. As all the sample plots were in a relatively homogeneous forest environment (at least within 30 m of the sample plot boundary is homogeneous), the sample plots vector file was used to extract the mean values of the spanned pixels. Through this method, high-quality remote sensing data for southern China can be effectively obtained, and the potential of using Sentinel-2 to map the FSV in southern China is increased. Such a potential is truly important in southern China, where forest plantations grow rapidly in order to meet the demand for wood products, and for ecological protection [109]. The dates of the selected remote sensing image are mainly based on two considerations. First, the trees in the forest grow slowly in one year. The difference between the acquisition time of the sample plot data and the remote sensing data is less than one year, which has little effect on the results. In addition, we also use the median value of the two-year growing season to reduce this effect. Furthermore, we consider that the spectral information between May and October is better, but the image in one year could not cover the entirety of Hunan Province, so we chose to use the data for two year periods. Although our prediction results were good, some limitations of the study should be noted. The first are the limitations of the Sentinel-2 Level-1C TOA data. While three mask steps were used to process the Sentinel-2 Level-1C TOA data, this approach could not eliminate the bidirectional reflectance effects from changing the sun-sensor-surface geometries between the acquisitions. Further processing steps that transform the TOA data to bottom-of-atmosphere (BOA) surface data would improve the results; however, the GEE platform did not offer an online tool for this purpose. The local tools that can convert large-scale Sentinel-2 TOA data to BOA data are limited. According to [PERSON] et al. [57], although the TOA data were used, the annual composites of Sentinel-2 input variables would suffice to minimize the bidirectional effects to an acceptable degree. In this study, since approximately two years of Sentinel-2 data were used to extract the band characteristics and vegetation indices, it would be acceptable for this purpose. The second limitation is the modeling algorithm. Although the RF algorithm has been proven to exhibit outstanding performance, the phenomena of the underestimation of high values and the overestimation of low values always exists. The innovation of the algorithms to solve this problem will be a future research direction. The third limitation is the value distribution of the sample data.