红外与毫米波学报2025，Vol.44Issue(6)：896-907,12.DOI:10.11972/j.issn.1001-9014.2025.06.008
基于注意力机制的陆地生态系统碳监测卫星大气层次识别与应用
Atmospheric layer identification and application of Terrestrial Ecosystem Carbon Inventory Satellite based on attention mechanism
摘要
Abstract
The Terrestrial Ecosystem Carbon Inventory Satellite(TECIS/CM-1)utilizes a combination of multi-beam li-dar,multi-spectral cameras,and other passive and active sensors for synergistic observations,enabling high-resolution,comprehensive,and three-dimensional atmospheric monitoring of clouds and aerosols.In recent years,traditional algo-rithms have faced challenges in terms of vertical layer retrieval accuracy and robustness in complex environments with low signal-to-noise ratios,near-surface observations,and mixed multi-layer structures.To address these issues,this pa-per proposes TECIS-CASNet,a generalized framework for atmospheric layer recognition and application,designed for the novel multi-beam lidar on the TECIS,leveraging the characteristics of the lidar data and deep learning attention mechanisms.To validate the reliability of this framework,the research team conducted multiple ground-based synchro-nous observation experiments to systematically evaluate its recognition accuracy.Finally,as a demonstrative applica-tion,the study focuses on a typical long-distance dust transport event in the Beijing-Tianjin-Hebei region of China,showcasing the practical application value of the framework.The results indicate that the TECIS-CASNet framework achieves high cloud-aerosol recognition accuracy,reaching 98.41%,and is capable of reducing misidentification and missed detection in complex environments,including low signal-to-noise ratios,near-surface layers,and multi-layer mixed structures.The absolute accuracy of aerosol optical depth retrieval is 0.01,with an overall accuracy of 98%.This paper,centered around the TECIS-CASNet framework,provides significant insights for lidar satellite atmospheric remote sensing data processing and environmental monitoring applications.关键词
陆地生态系统碳监测卫星/大气激光雷达/深度学习/注意力机制Key words
Terrestrial Ecosystem Carbon Inventory Satellite/atmospheric Lidar/deep learning/attention mechanism分类
天文与地球科学引用本文复制引用
卢庆锴,么嘉棋,李国元,马晨,刘诏,夏皓斌,徐浩钧,武建军..基于注意力机制的陆地生态系统碳监测卫星大气层次识别与应用[J].红外与毫米波学报,2025,44(6):896-907,12.基金项目
国家自然科学基金项目(42301501) （42301501）
自然资源高层次科技创新人才基金(B02202) Supported by the National Natural Science Foundation of China(42301501) （B02202）
the High-Level Science and Technology Innovation Talent Fund for Natural Resources(B02202) （B02202）
