Does the Satellite Sensing Column-Averaged Dry-Air Mole Fraction CO2 (XCO2) Require Oxygen Concentration From O2-A Band for Normalizing?

• Published in: IEEE transactions on geoscience and remote sensing Vol. 63; pp. 1 - 13
• Main Authors: Zhang, Lu, Cao, Xifeng, Yan, Huanhuan, Chen, Lin, Zhang, Peng, Zhang, Xingying, Gao, Peng, Liu, Gongju
• Format: Journal Article
• Language: English
• Published: New York IEEE 2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive optics; Air; Atmospheric measurements; Atmospheric water; Bias; Carbon; Carbon dioxide; Carbon dioxide concentration; Detectors; Error reduction; Extraterrestrial measurements; Glint; Greenhouse gases; Humidity; Mathematical models; Meteorological data; Meters; Mixing ratio; Normalization; O2–A band; Optical detectors; orbiting carbon observatory-2 (OCO-2); Remote sensing; Retrieval; satellite; Satellite broadcasting; Satellite instruments; Satellite observation; Satellite sensing; Satellites; Spaceborne remote sensing; Spectrometers; Systematic errors; Temporal resolution; Temporal variations; total carbon column observing network (TCCON); Water vapor; Water vapour
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2025.3575603

The measured carbon dioxide (CO2) mixing ratio is significantly influenced by atmospheric water vapor due to its rapid spatial-temporal variability. In greenhouse gas (GHG) remote sensing, the dry-air mixing ratio is commonly used to represent CO2 concentration, which reduces errors arising from water vapor fluctuations. Early satellite spectrometers were often equipped with the O2-A band to retrieve atmospheric O2 concentration. Leveraging the stable ratio between O2 and dry air, CO2 can be normalized to derive its dry-air mixing ratio. This approach not only reduces systematic errors but also plays a crucial role in minimizing instrumental biases, thereby enhancing measurement accuracy. However, advancements in satellite instrument calibration have diminished the necessity for O2-A band normalization. Therefore, this study proposes an alternative normalization method based on meteorological data to alleviate hardware requirements and costs. CO2 retrieval results from O2-A band normalization, and meteorological data normalization were compared using observations from the orbiting carbon observatory-2 (OCO-2) satellite. These results were validated against data from the total carbon column observing network (TCCON). The findings indicate that, in comparison to TCCON, the mean biases of the O2-A band scheme are 0.17, 0.25, and −0.005 ppm in glint (GL), nadir (ND), and target (TG) modes, whereas the biases for the meteorological normalization method are 0.35, 0.45, and 0.19 ppm, respectively. Overall, the systematic bias of the meteorological normalization method is approximately 0.2 ppm greater than that of the O2-A band scheme, potentially attributable to the spatial-temporal resolution limitations of the modern-era retrospective analysis for research and applications, version-2 (MERRA-2) dataset. Nonetheless, this method remains a viable alternative to O2-A band normalization for CO2 retrieval.