Review of satellite remote sensing of carbon dioxide inversion and assimilation

Hu, K. ORCID: https://orcid.org/0000-0001-7181-9935, Feng, X. ORCID: https://orcid.org/0000-0002-4584-7630, Zhang, Q. ORCID: https://orcid.org/0000-0002-2257-0405, Shao, P. ORCID: https://orcid.org/0000-0001-5001-7415, Liu, Z. ORCID: https://orcid.org/0000-0002-6856-9274, Xu, Y. ORCID: https://orcid.org/0000-0003-3564-399X, Wang, S., Wang, Y., Wang, H., Di, L. and Xia, M. ORCID: https://orcid.org/0000-0003-4681-9129 (2024) Review of satellite remote sensing of carbon dioxide inversion and assimilation. Remote Sensing, 16 (18). 3394. ISSN 2072-4292 doi: 10.3390/rs16183394

Abstract/Summary

With the rapid development of satellite remote sensing technology, carbon-cycle research, as a key focus of global climate change, has also been widely developed in terms of carbon source/sink-research methods. The internationally recognized “top-down” approach, which is based on satellite observations, is an important means to verify greenhouse gas-emission inventories. This article reviews the principles, categories, and development of satellite detection payloads for greenhouse gases and introduces inversion algorithms and datasets for satellite remote sensing of XCO2. It emphasizes inversion methods based on machine learning and assimilation algorithms. Additionally, it presents the technology and achievements of carbon-assimilation systems used to estimate carbon fluxes. Finally, the article summarizes and prospects the future development of carbon-assimilation inversion to improve the accuracy of estimating and monitoring Earth’s carbon-cycle processes.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/118574
Identification Number/DOI	10.3390/rs16183394
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
Publisher	MDPI AG
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