# Estimating Forest Carbon Stock Using Enhanced ResNet and Sentinel-2 Imagery
## 1 Introduction  
Forests play a pivotal role in the global carbon cycle by serving as major carbon sinks, thereby contributing to climate regulation, ecological stability, and terrestrial carbon sequestration [1, 2, 3, 4, 5]. Through photosynthesis, forests absorb atmospheric CO\({}_{2}\) and store it as organic carbon in biomass and soils, mitigating anthropogenic greenhouse gas emissions [6, 7, 8, 9, 10]. According to [PERSON] et al. [11, 12, 13, 14], global forests sequester approximately 2.4 \(\pm\) 0.4 \(\mathrm{P}\mathrm{c}\) annually--over one-fifth of global fossil fuel emissions--underscoring their essential role in international climate mitigation efforts [15, 16, 17, 18, 19]. As nations strive toward carbon neutrality under the Paris Agreement, accurate and spatially explicit forest carbon stock estimation has become increasingly vital for national emission inventories, REDD+ implementation, and sustainable forest management [20, 21, 22, 23]. China, aiming for peak carbon emissions by 2030 and achieving carbon neutrality by 2060, faces growing demands for scalable, high-resolution carbon monitoring solutions--particularly in ecologically fragile and topographically complex regions such asGuizhou Province [24; 25; 26; 27; 28]. The Liuchong River Basin, located in Bijie City, Guizhou, exemplifies this challenge, with pronounced elevation gradients, heterogeneous vegetation patterns, and significant spatial variability in forest structure [29; 30; 31; 32; 33]. These factors complicate traditional forest inventory and modeling approaches [34; 35; 36; 37; 38]. Historically, forest carbon stock estimation has depended on field-based inventories and allometric equations [39; 40; 41; 42], which, despite their plot-level accuracy, are labor-intensive and lack scalability. The advent of remote sensing has enabled broader spatial monitoring, with vegetation indices and topographic features derived from medium-resolution imagery (e.g., Landsat) commonly used in conjunction with statistical and machine learning algorithms (e.g., multiple linear regression, Random Forest, GBDT) for biomass estimation [43; 44; 45; 46; 47; 48]. However, the 30 m spatial resolution of Landsat limits its capacity to capture fine-scale canopy heterogeneity in rugged mountainous terrain, thus affecting model precision [49; 50]. The Sentinel-2 satellite constellation offers new opportunities for high-resolution forest carbon mapping, delivering multispectral imagery at 10-20 m resolution with a 5-day revisit cycle [51; 52; 53; 54]. Its broad spectral range--including visible, red-edge, and shortwave infrared bands--has proven effective for monitoring vegetation structure and health [55; 56; 57]. While several studies have leveraged Sentinel-2 imagery for forest biomass estimation using machine learning methods [58; 59; 60; 61], challenges remain regarding model generalizability across heterogeneous landscapes and complex topography. To address these challenges, deep learning has emerged as a powerful tool for geospatial modeling [62; 63; 64; 65]. Convolutional Neural Networks (CNNs), especially residual networks (ResNet), have demonstrated strong performance in remote sensing tasks due to their capacity for multiscale feature learning and end-to-end modeling [66; 67; 12; 68; 69]. Nevertheless, the application of deep architectures to forest carbon estimation remains underexplored, particularly with respect to spectral-spatial optimization in mountainous ecosystems. In this study, we propose a deep learning framework for forest carbon stock estimation that integrates Sentinel-2 multispectral data with an enhanced ResNet architecture. Our objective is to improve estimation accuracy and spatial adaptability under complex terrain conditions. The main contributions of this work are as follows:  
* Enhanced deep regression model: We develop a modified ResNet incorporating multiscale residual blocks and channel attention mechanisms to strengthen spatial context learning and spectral feature extraction for carbon modeling.