A machine learning based line-by-line absorption coefficient model for the application of atmospheric carbon dioxide remote sensing-Reference-Cited by-同舟云学术

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A machine learning based line-by-line absorption coefficient model for the application of atmospheric carbon dioxide remote sensing

Published:2023-02 Issue: Volume:296 Page:108441
ISSN:0022-4073
Container-title:Journal of Quantitative Spectroscopy and Radiative Transfer
language:en
Short-container-title:Journal of Quantitative Spectroscopy and Radiative Transfer

Author:

Xie Fengxin,Ren Tao,Zhao Ziqing,Zhao Changying

Funder

Japan Aerospace Exploration Agency

Natural Science Foundation of Shanghai

National Institute for Environmental Studies

National Natural Science Foundation of China

Ministry of Education of the People's Republic of China

Publisher

Elsevier BV

Subject

Spectroscopy,Atomic and Molecular Physics, and Optics,Radiation

Reference43 articles.

1. Radiative forcing and climate response;Hansen,1997

2. Changes in atmospheric constituents and in radiative forcing. chapter 2;Forster,2007

3. Chinas energy transitions for carbon neutrality: challenges and opportunities;Zhao;Carbon Neutral.,2022

4. A global CO2 flux dataset (2015–2019) inferred from OCO-2 retrievals using the tan-Tracker inversion system;Jin;Earth. Syst. Sci. Data Discuss.,2021

5. The status and development proposal of carbon sources and sinks monitoring satellite system;Meng;Carbon Neutral.,2022

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1. Improved Accuracy of XCO₂ Retrieval Based on OCO-2 Rtretrieval Framework Model;IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium;2023-07-16

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