An improved band design framework for atmospheric pollutant detection and its application to the design of satellites for CO2 observation

• Published in: Journal of quantitative spectroscopy & radiative transfer Vol. 309; p. 108712
• Main Authors: Wu, Zhaocong, Li, Mingliang, Rao, Keyi, Fang, Ruixin, Yue, Yixian, Xia, Anquan
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.11.2023
• Subjects: Atmospheric pollutants; Band design; CO2; Fuzzy comprehensive evaluation; HITRAN; SCIATRAN; Band design; SCIATRAN; Atmospheric pollutants; HITRAN; Fuzzy comprehensive evaluation; CO2
• ISSN: 0022-4073; 1879-1352
• DOI: 10.1016/j.jqsrt.2023.108712

•A band design framework BDF is built for remote sensing detection bands of gaseous atmospheric pollutants.•The fuzzy comprehensive evaluation model is improved (IFCE) to achieve a quantitative assessment of gaseous atmospheric pollutants absorption windows.•The BDF-IFCE band design method is proposed based on the BDF, the HITRAN database, the IFCE model, the sensitivity analysis and inversion algorithm.•The BDF-IFCE method quickly and accurately provides candidate bands for CO2 remote sensing detection. High-precision atmospheric remote sensing observation is one of the most challenging tasks in the key fields of earth observation. The accurate accounting for technical indicators such as the working spectral band, spectral resolution, and signal-to-noise ratio (SNR) of high-precision atmospheric observation satellites is an urgent research task. To address this need, this paper proposes a band design framework (BDF) that can replace airborne detection experiments to some extent and reduce the workload of satellite design. First, based on the HITRAN database, the absorption spectra of gases in the target gas absorption window are extracted as a band dataset. Then, membership functions are designed based on a trapezoid distribution, the monitoring factor α is introduced according to the importance of strong and weak absorption channels to obtain the improved fuzzy comprehensive evaluation (IFCE) model. Subsequently, the IFCE model is used to screen the band dataset. Finally, the bands are optimized based on the SCIATRAN model, sensitivity analysis and optimal inversion algorithm, and the BDF-IFCE band design method for remote sensing detection of gaseous atmospheric pollutants is established based on the improved fuzzy synthetic evaluation model. The BDF-IFCE method is applied to design the CO2 sensor detection band and two absorption windows are successfully identified (6170–6270 cm−1 and 6290–6380 cm−1). With a restricted range of aerosol AODs, detection accuracy of 3–4 ppm can be achieved with a signal-to-noise ratio of ∼300. The test results are the same as the band setting of existing atmospheric sensors, indicating that this method is effective and feasible in designing the CO2 remote sensor detection band.