Atmospheric CO2 retrieval from satellite spectral measurements by a two-step machine learning approach. (February 2022)

• Record Type: Journal Article
• Authors: Zhao, Ziqing; Xie, Fengxin; Ren, Tao; Zhao, Changying
• Is Part Of: Journal of quantitative spectroscopy & radiative transfer. Volume 278(2022)
• Publication Date: 2022-02
• Subjects: CO2 column density -- Machine learning -- Remote sensing -- GOSAT; Spectrum analysis -- Periodicals; Radiation -- Periodicals; Analyse spectrale -- Périodiques; Rayonnement -- Périodiques; Radiation; Spectrum analysis; Periodicals; 543.0858
• DOI: 10.1016/j.jqsrt.2021.108006
• Languages: English
• ISSNs: 0022-4073

Abstract

Highlights: An efficient two-step machine learning method for the retrieval of atmospheric X C O 2 is proposed. A forward radiative calculation model is presented to provide the training data. The method is tested to retrieve X C O 2 from the GOSAT spectral measurements. Abstract: In the present study, a two-step machine learning approach is proposed and tested based on the Greenhouse gases Observing SATllite (GOSAT) spectral measurements to efficiently retrieve the atmospheric CO 2 column density. A simple one-dimensional line-by-line forward radiation model is developed to simulate the GOSAT observed spectra, which is used to generate the training data for the two-step machine learning model. By using the two-step machine learning model, the atmospheric spectral optical thickness can be retrieved first, and then the CO 2 column density is retrieved from the optical thickness spectrum later. As a proof-of-concept study, the model has been applied to retrieve CO 2 column density in Australia for clear sky conditions from the GOSAT observed 1.6 μ m spectra. Results have shown that the accuracy of the proposed method for CO 2 column density retrieval is about 3 ppm but the efficiency is found to be excellent. Further improvements for improving the retrieval accuracy are discussed.