## 1 Introduction  
The forest stock volume (FSV, m\({}^{3}\) ha\({}^{-1}\)) is the sum of the stem volumes of all the living trees per unit area, and is one of key forest variables for forest resources management and assessments on local, region and country scales [1]. FSVs have a strong relationship with the aboveground biomass (AGB) and carbon stocks [2]. To understand the spatial distribution of carbon in forests and to derive predictions for monitoring carbon stock trends, the FSV must be quantified [3]. Traditionally, the FSV is estimated by sampling several plots, which involves substantial manpower, materials, and financial resources [4]. With the development of remote sensing technology, particularly the Landsat series since 1972, satellite imagery has played an important role in forest inventory. Various studies have been performed to estimate the forest variables using low spatial resolution (LSR, LSR \(\geq\) 30 m), moderate spatial resolution (MSR, 5 m \(<\) MSR \(<\) 30 m), and high spatial resolution (HSR, HSR \(\leq\) 5 m) [5] data obtained by different optical sensors (e.g., Landsat [6; 7; 8; 9; 10; 11], MODIS [12; 13; 14; 15; 16], SPOT [17; 18; 19; 20], Quickbird [21; 22; 23; 24; 25; 26], RapidEye [27; 28; 29; 30]), microwave sensors [31; 32; 33; 34], and light detection and ranging (LiDAR) sensors [35; 36; 37; 38; 39]. The successful applications of these technological tools have laid the foundation for the estimation of forest variables, such as the FSV, using remote sensing technology. Two recent developments in the Earth Observation (EO) sector have increased the potential to improve the efficiency of retrieving global forest attributes. The Sentinel-2A and Sentinel-2B satellite series launched by the European Space Agency (ESA) through its Copernicus program in 2015 (S2A) and 2017 (S2B) provide nominal five-day revisit imagery across the globe [40]. The Sentinel-2 imagery includes 13 spectral bands with spatial resolutions ranging from 10-60 m [40]. One of the main purposes of the Sentinel-2 satellite series is vegetation analysis [4]. The Sentinel-2 satellite images provided by the operational environment monitoring system based on the European Copernicus program may be accessed freely. The different spatial resolution bands, the short revisit period, and the rich spectral information have made these images a popular source of remote sensing data for forestry research in recent years. For instance, [PERSON] et al. [41] used Sentinel-2 data to classify common tree species in central Sweden, observing the highest overall accuracy, i.e., approximately 88.2%, using all the imagery bands in the final model. A study conducted by [PERSON] et al. [42] in southern Poland affirmed that the Sentinel-2 series could accurately delineate tree species (e.g., beech, oak, birch, alder, and larch) with an overall accuracy above 85%. Similarly, [PERSON] et al. [43] explored the ability of Sentinel-2 images to estimate the forest biomass in Nepal; their biomass estimation model achieved an \(R^{2}\) = 0.81 and RMSE = 25.57 t ha\({}^{-1}\). [PERSON] et al. [44] demonstrated the potential of Sentinel-2A data to estimate the chlorophyll content in open canopy conjor forests (\(R^{2}\) \(>\) 0.7 for June; \(R^{2}\) \(>\) 0.4 for December). In the Brazilian Amazon, [PERSON] et al. [45] performed comparative research on monitoring selective logging using Sentinel-2 and Landsat-8 OLI imagery. These authors found that Sentinel-2 data (43.2% detected) were more effective in detecting logging concessions than Landsat 8 data (35.5% detected). In Poland, [PERSON] et al. [46] used the Sentinel-2 time series to map forest stand species in the Carpathian Mountains and reported higher accuracy, i.e., a 5-10% improvement in overall accuracy compared with only using single date imagery. These studies demonstrate the potential of Sentinel-2 for forest vegetation monitoring.