## 1 Introduction  
Passive Fourier transform remote sensing technology enables the detection and identification of chemical pollutants in the air [1], and has been widely studied and applied in many fields such as the detection of hazardous clouds [2; 3; 4; 5; 6; 7], the analysis of aircraft exhaust plume [8; 9; 10], pollution gas emissions over distance monitoring [11; 12; 13; 14; 15] and taking measurements of greenhouse gases [16; 17; 18]. The Fourier transform infrared spectrometer is mainly carried by a vehicle or fixed on a tripod which is slow to deploy or requires dedicated runways [19]. The unmanned aerial vehicle is an aerial vehicle that is guided automatically with a remote controller and is implemented in the agriculture, oil & gas industries, and seaports for inspection, security, and surveillance [20]. UAVs have become attractive experimental platforms for air quality research in recent years. They have the advantages of being low cost, having wide coverage and multiple observation angles, as well as the capability to perform remote sensing and close-in detection which do not require equipment operators to get close to toxic gases and are very safe. UAVs have demonstrated their applications in multiple studies, such as integrating a tunable diode laser absorption spectroscopy (TDLAS) system to detect natural gas fugitive leaks [21] and using particulate matter and nitrogen dioxide sensors to monitor atmospheric pollutants [22]. Despite the potential of UAV remote sensing gas detection, the Fourier transform infrared spectrometer generally contains a motor-driven interferometer module, a cooledinfrared detector, a data acquisition board and a processing computer. The weight and power constraints limit the gas detection capabilities of autonomous UAVs. [PERSON] et al. suspended the Fourier transform spectrometer under the DJI M600 drone (DJI, Shenzhen, China) to obtain GPS-referenced concentration data for carbon dioxide, propane and water (humidity) [19]. Limited by the maximum lifting weight of the rotary-wing UAV, the endurance of the drone was short. Aside from lift weight and power constraints, vibration resistance and high stability are also required by FTIR systems. The interference fringes of the spectrometer should be kept constant before and after takeoff to ensure that high-quality spectra are recovered during flight. In addition, the pure background spectrum is difficult to measure because of the vibration and attitude of the UAV platform. The method of directly subtracting the background spectrum from the target spectrum may increase the false alarm rate and reduce the detection probability of gases with weak spectral characteristics. [PERSON] et al. proposed a brightness temperature method based on [PERSON]'s radiation law [1]. In the brightness temperature spectrum regime, the temperature spectrum has a constant baseline for many natural materials that serve as the background in field measurements (concrete, forest, etc.). The brightness temperature method measures blackbody spectra at different temperatures to calibrate the spectrometer before each measurement. If the blackbody is designed inside the spectrometer, the increased weight makes it difficult to integrate with drones. The external calibration requires strict environmental conditions for the UAV-integrated system applied in the field. [PERSON] et al. used the least absolute shrinkage and selection operator (LASSO) method to select the most appropriate reference for spectral fitting [7]. The accuracy of fitting decreases when the atmospheric state changes due to poor meteorological conditions or the turbulence effects associated with the complex air flow around a UAV. In order to make the Fourier transform infrared spectrometer used in more scenarios and make full use of the advantages of UAV platforms, we proposed an unmanned helicopter airborne dual-channel parallel oscillating mirror FTIR passive remote sensing system, different from the rotary-wing UAV used in previous studies.