# Estimating Forest Carbon Stock Using Enhanced ResNet and Sentinel-2 Imagery
## 2 Materials
### Data Sources  
This study integrates ground-based forest inventory measurements with Sentinel-2 multispectral imagery to estimate plot-level aboveground carbon stock in the Liuchong River Basin, Guizhou Province.  
(1) Ground-Based Forest Inventory Data: Field data were collected from 150 circular sample plots (15 m radius, approximately 707 m\({}^{2}\)) strategically distributed across the county's representative forest ecosystems. Tree-level attributes such as species, diameter at breast height (DBH \(\geq\) 5 cm), total height, and crown width were recorded using standardized protocols (see Figure 1). GNSS receivers with sub-meter accuracy were used for geolocation.  
Figure 1: Location and topographic features of the study area (Liuchong River Basin, Guizhou, China).  
Aboveground biomass (AGB) per tree was estimated using species-specific allometric equations tailored to regional forest types. Plot-level AGB was then aggregated and converted to carbon stock (Mg/ha) using the standard IPCC biomass-to-carbon conversion factor of 0.5. Each plot was spatially matched to a 640 m \(\times\) 640 m Sentinel-2 patch centered on its geolocation, which also served as the input region for model training. The sampled plots cover a broad range of vegetation types, including _Pinus yunnensis_, _Quercus variabilis_, and mixed deciduous stands, as well as an elevation gradient from 2000 m to 2800 m, thus ensuring ecological diversity and modeling robustness.  
The 150 sample plots cover a representative range of forest types in the Liuchong River Basin, including mixed deciduous-coniferous stands, evergreeniferous forests (e.g., _Pinus yunnensis_), and broadleaf deciduous forests (e.g., _Quercus variabilis_). The estimated aboveground biomass (AGB) values derived from the inventory plots span from approximately 40 to 150 Mg/ha, ensuring that the training dataset captures both low-density and high-density forest conditions. This diversity supports robust model learning across ecological gradients.  
(2) Sentinel-2 Multispectral Imagery: Sentinel-2 Level-2A surface reflectance data were retrieved from the Copernicus Open Access Hub. Four cloud-free scenes captured between May and October 2022 were selected to minimize seasonal and atmospheric effects and composited into a seamless mosaic of the study area.  
Preprocessing included atmospheric correction (Sen2 Cor), cloud and shadow masking using the Scene Classification Layer (SCL), and bilinear resampling of all bands to a consistent 20 m resolution. A total of 10 spectral bands with high relevance to forest structure and biomass estimation were retained, including visible, red-edge, near-infrared (NIR), and shortwave infrared (SWIR) bands, as detailed in Table 1. Specifically, we implemented a Cosine Correction (C-correction) model using the 30-meter SRTM Digital Elevation Model (DEM). This method normalizes the reflectance of each pixel by computing the cosine of the solar incidence angle relative to the local terrain slope and aspect, thereby adjusting reflectance values to compensate for varying illumination conditions.