A review of satellite-based CO2 data reconstruction studies: methodologies, challenges, and advances

Hu, K. ORCID: https://orcid.org/0000-0001-7181-9935, Liu, Z. ORCID: https://orcid.org/0000-0002-6856-9274, Shao, P. ORCID: https://orcid.org/0000-0001-5001-7415, Ma, K. ORCID: https://orcid.org/0000-0001-9714-8930, Xu, Y. ORCID: https://orcid.org/0000-0003-3564-399X, Wang, S., Wang, Y., Wang, H., Di, L., Xia, M. ORCID: https://orcid.org/0000-0003-4681-9129 and Zhang, Y. (2024) A review of satellite-based CO2 data reconstruction studies: methodologies, challenges, and advances. Remote Sensing, 16 (20). 3818. ISSN 2072-4292 doi: 10.3390/rs16203818

Abstract/Summary

Carbon dioxide is one of the most influential greenhouse gases affecting human life. CO2 data can be obtained through three methods: ground-based, airborne, and satellite-based observations. However, ground-based monitoring is typically composed of sparsely distributed stations, while airborne monitoring has limited coverage and spatial resolution; they cannot fully reflect the spatiotemporal distribution of CO2. Satellite remote sensing plays a crucial role in monitoring the global distribution of atmospheric CO2, offering high observation accuracy and wide coverage. However, satellite remote sensing still faces spatiotemporal constraints, such as interference from clouds (or aerosols) and limitations from satellite orbits, which can lead to significant data loss. Therefore, the reconstruction of satellite-based CO2 data becomes particularly important. This article summarizes methods for the reconstruction of satellite-based CO2 data, including interpolation, data fusion, and super-resolution reconstruction techniques, and their advantages and disadvantages, it also provides a comprehensive overview of the classification and applications of super-resolution reconstruction techniques. Finally, the article offers future perspectives, suggesting that ideas like image super-resolution reconstruction represent the future trend in the field of satellite-based CO2 data reconstruction.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/119220
Identification Number/DOI	10.3390/rs16203818
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
Publisher	MDPI AG
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