Atmospheric environmental problems such as air pollution and greenhouse gas emissions not only impact climate change but also seriously threaten human life. Both greenhouse gas emissions reduction and air pollution control are related to changes in atmospheric compositions. In the context of “double carbon”, China aims to increase its national contribution, striving to peak carbon dioxide emissions by 2030 and achieve carbon neutrality by 2060. The basis for all policies formulated on atmospheric environmental improvement relies on accurate data of air pollution and greenhouse gas emissions. For this reason, the solution to atmospheric environmental problems depends on accurate monitoring technologies and forecasting methods of atmospheric parameters. Lidar has obvious advantages in atmospheric parameters monitoring because of its high spatial and temporal resolution, high sensitivity, real-time operation, non-contact, etc. To achieve compactness and lightweight design suitable for various load platforms, the all-fiber coaxial lidar is an attractive option. In recent years, all-fiber lidar has been widely applied in atmospheric parameters measurement due to its flexible transformation of light beams, less susceptibility to temperature, pollution, and other environmental factors, and ability to achieve higher precision measurement. Compared with the biaxial system, the all-fiber coaxial system has significant advantages, such as low cost, simple, compact and stable structure, and small blind zone. Meanwhile, the amplified spontaneous emission (ASE) noise from the fiber amplifier is inevitable for a coaxial lidar system and degrades the performance of the lidar system. The ASE backscattering from specular reflection results in a decreased signal-to-noise ratio, shortened effective measurement distance, and even misidentification. The ASE noise of the amplifier could be regarded as a fingerprint function. To improve the performance of the all-fiber coaxial lidar, a method for calibrating ASE noise is proposed and changes in mirror reflectivity of telescope and laser power have been included. By calibrating the function of the ASE noise of the fiber amplifier in a lidar system, the ASE noise of the all-fiber coaxial lidar is mitigated, thus improving the signal-to-noise ratio and performance of the all-fiber coaxial lidar.

To acquire accurate data for all-fiber coaxial lidar, it is necessary to remove the ASE noise, requiring a calibration method for ASE. Coaxial lidar and biaxial lidar simultaneously measure atmospheric backscattering signals along the same optical path. The backscattering photon counts received by both the coaxial and biaxial systems are first denoised. Subsequently, the denoised photon counts are normalized to indicate those received by the coaxial system. By comparing the backscattering data from the coaxial lidar with that from the biaxial lidar, specifically subtracting the biaxial system’s photon counts from the coaxial system’s, the ASE noise is revealed. The ASE noise function, which can be seen as a “fingerprint function”, is derived by fitting the ASE noise data. This ASE noise function enables the determination of the true photon counts for the coaxial lidar and effectively mitigates the ASE noise. Atmospheric aerosol extinction coefficients and other atmospheric parameters are then derived from the true data. In the case of laser power or telescope specular reflectivity changes due to long-term use or operation in a poor environment, the ASE noise function may also change. By measuring the laser power or reflectivity of the telescope, estimating the ratio of variation, and adjusting the ASE noise function accordingly, accurate results can be obtained without needing to recalibrate the ASE noise function. Additionally, time-sharing optical switching allows for accurate background noise measurement of the detector.

An experiment has been conducted to verify the validity of the calibration method of ASE noise by comparing the aerosol extinction coefficient data retrieved from the coaxial lidar with the biaxial lidar. The measured data and the fitted function of ASE noise of coaxial lidar are shown, and the ASE noise function agrees with the measured data perfectly [Fig. 2(a)]. The photon counts of coaxial lidar after ASE noise mitigation are compared with biaxial lidar, and the result shows that the ASE noise function could be applied to mitigate ASE noise in coaxial lidar [Fig. 2(b)]. A field experiment conducted in Hefei on October 25, 2021, was performed to verify the effectiveness and reliability of this method. For a coaxial lidar, after the measured data are denoised and ASE noise mitigated, the backscattering photon counts are obtained. The extinction coefficients are retrieved with the photon counts by means of Fernald’s method. Extinction coefficient data retrieved for coaxial and biaxial single-photon lidar are compared (Fig. 3), and the relative deviation of data of coaxial lidar with biaxial lidar is shown [Fig. 3(b)]. The result shows that the atmospheric extinction coefficient obtained by the coaxial lidar agrees well with that of the biaxial lidar, with a maximum difference between ±10%. The result indicates that the calibration method could mitigate ASE noise effectively.

The ASE noise from the fiber amplifier is inevitable and degrades the performance of all-fiber coaxial lidar significantly. The ASE backscattering from specular reflection results in decreased signal-to-noise ratio, shortened effective measurement distance, and even misidentification. A calibration method is proposed by comparing the backscattering data received from the coaxial lidar with that of the biaxial lidar, and then the ASE noise function is derived. The ASE noise is subtracted from the backscattering data of coaxial lidar and true data of photon counts is obtained by the ASE noise function. An experiment has been conducted and verified the validity of the method by comparing the aerosol extinction coefficient data retrieved from the coaxial lidar with the biaxial lidar. The results show that the method could effectively improve the performance of coaxial lidar with a data relative deviation of less than ±10% when compared with biaxial lidar. The calibration method effectively improves the performance of the coaxial lidar. Additionally, changes in the mirror reflectivity of the telescope and laser power have been included in the method.