Study of mid-latitude retrieval XCO2 greenhouse gas: Validation of satellite-based shortwave infrared spectroscopy with ground-based TCCON observations

• Published in: The Science of the total environment Vol. 836; p. 155513
• Main Authors: Karbasi, Samira, Malakooti, Hossein, Rahnama, Mehdi, Azadi, Majid
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 25-08-2022
• Subjects: ACOS algorithm; GOSAT satellite; K-means clustering; NIES algorithm; Observing network (TCCON); Remo. Tec. Algorithm; XCO2 retrieval; GOSAT satellite; ACOS algorithm; XCO2 retrieval; Remo. Tec. Algorithm; Observing network (TCCON); NIES algorithm; K-means clustering
• ISSN: 0048-9697; 1879-1026
• DOI: 10.1016/j.scitotenv.2022.155513

Carbon dioxide (CO2) is a major greenhouse gas. This study investigated the performance of three common algorithms, namely NIES, ACOS, and Remo Tec (SRFP). These algorithms were compared using GOSAT observation satellite data with reference data obtained from TCCON during the period 2009–2021. According to statistical evaluation, the SRFP and NIES algorithms achieved the lowest and highest correlation values of the 13 year (2009_2021) average of all sites, respectively. The average bias error values of NIES and ACOS was estimated to be less than that of SRFP approximately 0.5 ppm, while the bias within SRFP was of about 2 ppm. Comparing the RMSE and CRMS error values showed that the highest and lowest error values were related to the SRFP and NIES algorithms respectively, which were 0.37–1.67 and ppm 1.46–7.9. The researchers also compared them with monthly time changes based on ground measurements, and observed a time series of CO2 concentration changes that well matched the trend of gas concentration values at ground stations obtained by NIES algorithm. The results showed that in most cases NIES was an effective algorithm to retrieve carbon dioxide gas concentrations, allowing the researchers to identify the main sources of greenhouse gas emissions in different areas. The clustering result in the study area showed that the continental CO2 columnar concentration has a specific seasonal cycle, with the maximum and minimum values appearing in winter-early spring and spring-late summer, respectively. In conclusion, cluster analysis can classify the surface CO2 column concentration values and determine the spatial distribution pattern of CO2.
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•The daily SWIR measurements of CO2 from the GOSAT Satellite from NIEs, ACOS and SRFP algorithms were collected.•The retrieval CO2 algorithms are evaluated to introduce the best one for mid-latitude according to the TCCCON 12 years ground-based observation.•The NIES and the ACOS algorithms were significantly recognized successful compared to the SRFP.•In the most cases, the NIES method is more accurate, skillful and reliable than the ACOS and SRFP method.•The mid-latitude was spatially partitioned according to the NIES monthly average column CO2 and k-Means cluster analysis during the period 2021–2009.