内蒙古农业大学学报（自然科学版）2026，Vol.47Issue(5)：89-100,12.DOI:10.16853/j.cnki.1009-3575.2026.05.010
基于OCO-3与ERA5数据的双分支大气CO2浓度反演网络
Dual-branch Feature-enhanced Network for Atmospheric CO2 Concentration Retrieval Based on OCO-3 and ERA5 Data
摘要
Abstract
Atmospheric CO₂ retrieval from satellite observations is often hindered by orbital variability,clouds,and aerosols,result-ing in degraded accuracy under complex aerosol conditions.To address these challenges,this study introduced a CO₂ Concentration Feature-Merging Dual-Branch Network(CCFM-DBNet),a dual-branch feature-enhanced architecture designed for precise atmo-spheric CO₂ inversion.The spectral branch integrated a Global Grouped Coordinate Attention(GGCA)module into a 2D convolu-tional backbone to strengthen responses to key CO₂ absorption bands while suppressing aerosol and water-vapor interference.The me-teorological branch employed a Multi-Scale Residual Attention(MSRA)module that combines multi-scale convolutions,residual connections,and channel attention to effectively capture vertical meteorological profiles.Feature vectors from both branches were fused along the feature dimension,enabling complementary physical information and achieving end-to-end XCO₂ prediction.Experi-mental results demonstrated that the CCFM-DBNet increased the coefficient of determination(R²)by 0.65%and reduced RMSE and MAE by 7.83%and 9.02%,respectively,compared with the baseline model.These improvements confirmed the method's effec-tiveness in enhancing XCO₂ retrieval accuracy under complex atmospheric conditions.关键词
XCO2反演/双分支特征增强网络/OCO-3/ERA5/全局分组注意力Key words
XCO₂ retrieval/Dual-branch feature-enhanced network/OCO-3/ERA5/Global grouped coordinate attention分类
资源环境引用本文复制引用
邓天民,赵西崇,彭扬洋..基于OCO-3与ERA5数据的双分支大气CO2浓度反演网络[J].内蒙古农业大学学报（自然科学版）,2026,47(5):89-100,12.基金项目
重庆市技术创新与应用发展专项重点项目(CSTB2022TIAD-KPX0118) （CSTB2022TIAD-KPX0118）
国家重点研发计划项目(2022YFC3800502) （2022YFC3800502）
重庆市技术创新与应用发展专项重点项目(CSTB2022TIAD-KPX0113) （CSTB2022TIAD-KPX0113）
