## 1 Introduction
A typical traditional remote sensing image processing approach includes data downloading and local computer processing, which render image processing computationally demanding, and also hinder the processing capability for large datasets. Regarding imagery processing, the development of cloud computing technology has been fundamentally changing traditional remote sensing image processing. The Google Earth Engine (GEE) portal is a powerful cloud-based computing platform for image processing [47; 48]. The GEE archives massive, publicly-available remote sensing data, provides a programming environment, programming tools, and virtual machines for users with relatively simple code, and can process imagery data online. The GEE greatly improves the processing efficiency when using substantial amounts of remote sensing data. In recent years, the GEE was used in land cover mapping [49; 50; 51; 52; 53; 54; 55; 56; 57; 58], agricultural applications [59; 60; 61; 62; 63], disaster management, and earth sciences studies [64; 65; 66]. This remote sensing data processing cloud platform makes the rapid processing of Sentinel-2 images covering large areas possible. In the FSV estimation field, some studies have examined assessments of the FSVs using remote sensing. For instance, [PERSON] et al. [67] found the model prediction of the plot-level growing stock volume using satellite images and field data to be useful; the result showed that the adj-\(R^{2}\) increased from 0.19 to 0.42. Using the random forests (RF) regression algorithm, [PERSON] et al. [68] estimated the FSV based on Sentinel-2 image, which provided relatively better results (\(R^{2}\) = 0.63, RMSE = 63.11 m\({}^{3}\) ha\({}^{-1}\)) than Landsat-8 OLI images (\(R^{2}\) = 0.62, RMSE = 64.40 m\({}^{3}\) ha\({}^{-1}\)). However, some studies were conducted that combined optical images and microwave data to estimate forest variables [69; 70; 71]. A noteworthy study was conducted by [PERSON] et al. [72]; these authors assessed the multiple linear regression (MLR) models built by Sentinel-1, Sentinel-2, and ALOS PALSAR-2 images to predict the FSV, and found that Sentinel-2 images performed best with an RMSE\(r\) = 42.03% and a pseudo-\(R^{2}\) = 0.63. For predicting forest variables, [PERSON] et al. showed that machine learning algorithms were likely to become more attractive in remote sensing [73]. These authors suggest that future studies using more methods, large areas, and Sentinel-2 data to predict the FSV should be conducted. However, ground plot survey data are still indispensable for remote sensing modeling [74]. The costs of ground plot surveys have always been high, which has presented some obstacles to the estimation of the provincial FSV by remote sensing. In addition, the traditional sample location survey technology often produces serious positional deviations, which may impact the modeling accuracy of the plot data-based remote sensing estimations and predictions [75]. Thus, traditional sample location technology is also an important reason for the inaccurate matching of sample plots and pixels, which results in estimation bias. Furthermore, to the best of our knowledge, little to no research has been conducted to compare the results of the RF, support vector regression (SVR), and MLR using Sentinel-2 images to predict FSVs. In addition, no FSV mapping has been conducted in Hunan Province, and this research gap directly affects forest policy making and management. Moreover, Hunan Province is located in southern China, where cloudy conditions frequently occur; these conditions are a great challenge to mapping the FSV in Hunan Province using remote sensing images.