Guest Editors
Dr. Xin Ma
Email: maxinwhu@whu.edu.cn
Affiliation: State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Luoyu Road No.129, Wuhan, 430079, China
Homepage:
Research Interests: Environmental research; Lidar remote sensing
Dr. Boming Liu
Email: liuboming@whu.edu.cn
Affiliation: State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430079, China
Homepage:
Research Interests: Lidar, Remote Sensing
Summary
The atmospheric environment is critical to ecological balance and human well-being, yet rapid urbanization, industrialization, and climate change have disrupted its composition, intensifying challenges such as air pollution and greenhouse gas emissions. Satellites like OCO-2/3, GOSAT, and Sentinel-5P have revolutionized atmospheric monitoring, providing high-resolution spatiotemporal data for carbon emissions, greenhouse gas dynamics, and air quality assessment. This Special Issue focuses on cutting-edge research leveraging remote sensing technologies for carbon monitoring and atmospheric analysis, encompassing advancements in sensors, algorithms, and integrated applications to enhance our understanding of atmospheric processes and support sustainable environmental management.
Topics of Interest:
We welcome original research articles, reviews, and case studies on topics including, but not limited to:
[1] Advances in greenhouse gas monitoring: Applications of satellite platforms such as OCO-2/3, GOSAT-2, and Sentinel-5P for tracking CO₂, CH₄, and other greenhouse gases.
[2] Machine learning in atmospheric monitoring: Integration of machine learning for analyzing spatiotemporal greenhouse gas patterns and predicting trends.
[3] Development of remote sensing algorithms: Novel methods for retrieving greenhouse gas concentrations and atmospheric parameters from satellite observations.
[4] Regional and global case studies: Applications of remote sensing technologies in monitoring urban carbon footprints, industrial emissions, and natural carbon sources and sinks.
[5] Data assimilation and modeling: Integration of remote sensing data into atmospheric models for improved carbon flux estimation and emission forecasting.
Keywords
Geospatial; Carbon monitoring; Remote sensing algorithms; Machine learning; Regional and global monitoring; Atmospheric detection; Greenhouse gases
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