Abstract
Carbon dioxide (CO 2 ) is one of the most important anthropogenic greenhouse gases (GHG) and significantly affects the energy balance of atmospheric systems. Larger coverage and higher spatial resolution of CO 2 measurements can complement the existing in situ network and satellite measurements and thus improve our understanding of the global carbon cycle. In this study, we present a self-made airborne infrared remote sensing spectrometer (Air-IRSS) designed to determine the regional distribution of CO 2 . The Air-IRSS measured CO 2 in the spectral range between 1590 and 1620 nm at a spectral resolution of 0.45 nm and an exposure time of 1 s. It was operated onboard an aircraft at a height of 3 km with a velocity of 180 km/h, and a spatial resolution of 50.00 m × 62.80 m. Weighting function modified differential optical absorption spectroscopy (WFM-DOAS) was used to analyze the measured spectra. The results show that the total uncertainty estimated for the retrieval of the CO 2 column was 1.26% for airborne measurements over a large region, and 0.30% for a fixed point, such as power points or factories. Under vibration-free static conditions, the on-ground Air-IRSS observations can adequately reproduce the variations observed by Greenhouse Gases Observing Satellite (GOSAT) with a correlation coefficient (r) of 0.72. Finally, we conducted an airborne field campaign to determine the regional distribution of CO 2 over the North China Plain. The regional distribution of CO 2 columns over four cities of Xing-tai, Hengshui, Shijiazhuang, and Baoding were obtained with the GPS information, which ranged from 2.00 × 10 21 molec cm -2 to 3.00 × 10 21 molec cm -2 . The CO 2 vertical distributions were almost uniform below a height of 3 km in the area without CO 2 emission sources, and the highest values were found over Baoding City.
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Wang, R., Xie, P., Xu, J., Li, A., & Sun, Y. (2019). Observation of CO 2 regional distribution using an airborne infrared remote sensing spectrometer (Air-IRSS) in the North China Plain. Remote Sensing, 11(2). https://doi.org/10.3390/rs11020123
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