Long-term mapping of aboveground carbon storage in China (2000−2020) using a prior-knowledge-constrained deep learning model

Abstract

Terrestrial ecosystems play a critical role in mitigating climate change by sequestering atmospheric carbon dioxide (CO₂), with aboveground carbon storage (AGC) serving as a key indicator of ecosystem carbon sink capacity. However, existing AGC estimation approaches remain constrained by the insufficient integration of natural and anthropogenic drivers, high update costs, and considerable uncertainties. In this study, we developed a prior-knowledge-constrained attention U-Net framework that incorporates land-use-specific carbon density information to improve the consistency and stability of AGC estimation. By integrating multiple environmental drivers, the framework effectively captures the nonlinear relationships between environmental factors and AGC, thereby enabling spatially continuous estimation of aboveground carbon storage across China's terrestrial ecosystems. The proposed model achieved an overall accuracy of 75.97% while significantly reducing estimation uncertainty. Based on this framework, a 1 km resolution AGC dataset for China was generated. The results indicate that forests are the dominant contributors to national AGC, with an annual average of 6.28 × 103 Tg C, accounting for 66.66% of the total AGC. Overall, this study provides an improved methodological framework for long-term AGC estimation and offers reliable data support for carbon sink assessment and carbon neutrality planning.