Application of Remote Sensing Technology in Monitoring the Carbon Dioxide Content in the Atmosphere

Houhua Feng; Zeyu Wang; Xiangyue Zhang

doi:10.62051/ybdddr72

Authors

Houhua Feng
Zeyu Wang
Xiangyue Zhang

DOI:

https://doi.org/10.62051/ybdddr72

Keywords:

Atmospheric Carbon Dioxide Content Monitoring; Satellite Remote Sensing; Application; Carbon Reduction; Carbon Sinks.

Abstract

Monitoring the carbon dioxide (CO2) concentration in the atmosphere is crucial for reducing carbon emissions. Compared to traditional ground-based monitoring, remote sensing measurements have a wider coverage and stronger continuous monitoring capabilities. This paper introduces the types and characteristics of remote sensing satellites used for CO₂ detection, describes the detection process and main technologies, and takes the remote sensing satellite carrying AIRS (Atmospheric Infrared Sounder) as an example to analyze its application in CO₂ detection. The results indicate that the current remote sensing satellites have TIROS-N series TOVS sensors, AIRS, IASI, SCIAMACHY, GOSAT, and OCO. The remote sensing technology process includes mission planning and sensor selection, data acquisition, data preprocessing, spectral analysis, inversion algorithm for extracting CO₂ concentration information, spatial resolution optimization, data quality control and validation, spatiotemporal analysis and data fusion, generation of visual reports, and data dissemination. The data obtained from the AIRS remote sensing satellite for detecting CO₂ concentration has been validated through comparison with ground station and aircraft measurement data. In addition, by analyzing the spatial and temporal distribution of CO₂ and analyzing the intensity and seasonal variations of carbon sources and sinks in different regions, high carbon emission areas can be identified. Remote sensing technology also has some challenges and uncertainties, such as limitations caused by atmospheric turbulence, cloud cover, and lower accuracy. Therefore, future research needs to optimize remote sensing data processing methods further, develop more accurate inversion models, and consider integrating multiple remote sensing data sources.

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References

[1] Hong X, Zhang C, Tian Y, Zhu Y, Hao Y, Liu C. First TanSat CO2 retrieval over land and ocean using both nadir and glint spectroscopy. Remote Sensing of Environment, 2024, 304: 114053.

[2] Shi G Y, Dai T, Xu N. Recent progress in the study of atmospheric CO2 concentration detected by satellite remote sensing. Advances in Earth Sciences, 2010, 25 (1): p407.

[3] Chen J, Gao M, Huang S, Hou W. Application of remote sensing satellite data for carbon emissions reduction. Journal of Chinese Economic and Business Studies, 2021, 19 (2): 109 – 117.

[4] Hu K, Zhang Q, Gong S, Zhang F, Weng L, Jiang S, Xia M. A Review of Anthropogenic Ground-Level Carbon Emissions Based on Satellite Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2024, 17: 8339 – 8357.

[5] Yang D, Hakkarainen J, Liu Y, Ialongo I, Cai Z, Tamminen J. Detection of Anthropogenic CO2 Emission Signatures with TanSat CO2 and with Copernicus Sentinel-5 Precursor (S5P) NO2 Measurements: First Results. Advances in Atmospheric Sciences, 2023, 40 (1): 1 – 5.

[6] Yu S, Zhang Z, Xia H, Dou X, Wu T, Hu Y, Li M, Shangguan M, Wei T, Zhao L, Wang L, Jiang P, Zhang C, You L, Tao L, Qiu J. Photon-counting distributed free-space spectroscopy. Light: Science & Applications, 2021, 10 (1): 212.

[7] Bai W, Zhang X, Zhang P. Temporal and spatial distribution of tropospheric CO2 over China based on satellite observations. Chinese Science Bulletin, 2010, 55 (31): 3612 – 3618.

[8] Deng A J, Guo H B, Hu J, Jiang C Z, Liu P G and Jing H F. Analysis of temporal and distribution characteristic of CO2 concentration over China based on GOSAT satellite data. Journal of Remote Sensing (Chinese), 2020. 24 (3): 319 - 325.

[9] Neumann PP, Asadi S, Bennetts VH, Lilienthal AJ, Bartholmai M. Monitoring of CCS areas using micro unmanned aerial vehicles (MUAVs). Energy Procedia. 2013, 37: 4182 - 90.

[10] Wang W, He J, Feng H, Jin Z. High-Coverage Reconstruction of XCO2 Using Multisource Satellite Remote Sensing Data in Beijing–Tianjin–Hebei Region. International Journal of Environmental Research and Public Health. 2022, 19 (17): 10853.