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A Remote Sensing Technique for CO2 Column Density

Ground-based remote sensing, as a critical technique for monitoring atmospheric greenhouse gas column density and calibrating satellite data, provides robust scientific support for establishing carbon accounting systems, studying regional carbon… Click to show full abstract

Ground-based remote sensing, as a critical technique for monitoring atmospheric greenhouse gas column density and calibrating satellite data, provides robust scientific support for establishing carbon accounting systems, studying regional carbon cycles, assessing the effectiveness of carbon reduction policies, and analyzing the dynamics of carbon sources and sinks. We have developed a novel greenhouse gas remote sensing device, hyperspectral atmospheric greenhouse gas remote sensor (HAGRES), which uses a grating spectrometer and a home-built solar tracker to determine atmospheric CO2 column density by analyzing solar spectra in the 1597.4–1618-nm band. The CO2 column density results were validated against temporally coinciding on-site measurements taken with the Total Carbon Column Observing Network (TCCON) IFS125HR spectrometer. Over a year of comparative observations, the mean difference between the device and TCCON data was ( $0.12~\pm ~0.23$ )%, with a correlation coefficient of $R =0.98$ . Additionally, CO2 data from the Greenhouse Gases Observing Satellite (GOSAT) around Hefei were compared with ground-based results, demonstrating correlation coefficients of 0.95 with TCCON and 0.92 with HAGRES. HAGRES has operated automatically and stably outdoors for twelve-months, showing strong optical stability and environmental adaptability after applying waterproof protection treatment. These results suggest that HAGRES is a cost-effective tool for satellite validation and carbon budget studies, supporting effective emission reduction strategies and contributing to China’s carbon neutrality goals.

Keywords: carbon; column density; column; remote sensing; co2 column

Journal Title: IEEE Transactions on Geoscience and Remote Sensing
Year Published: 2025

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