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Multiple environmental elements laser remote sensing method based on direct scattering spectrum
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摘要
激光雷达以其具有主动发射、高测量精度、良好的实时性和高时空分辨率等特点，在环境要素遥感中发挥着重要作用。直接散射光谱激光雷达可以基于散射光谱与介质环境的耦合关系，通过直接测量能量维度和光谱维度多特征信息诸如能量、频移、线宽等进行多环境要素的反演。文章对近年来直接散射光谱激光雷达在光谱特性研究技术和光谱探测技术两个方面取得的进展进行了归纳分析和总结，主要介绍了基于直接散射光谱的水下和大气多环境要素探测理论和反演模型，以及目前已有的多种直接散射光谱的测量方法。
Abstract
LiDAR plays an important role in the remote sensing of environmental elements due to its active emission, high detection accuracy, good real-time performance, and high spatial and temporal resolution. Based on the coupling relationship between the scattering spectrum and the medium environment, the direct scattering spectrum LiDAR can invert the multiple environment elements by directly measuring the energy dimension and the spectral dimension multi-feature information such as energy, frequency shift, linewidth, etc. In this paper, the recent advances in spectrum characteristics research and spectrum detection techniques of direct scattering spectrum LiDAR are briefly summarized. The detection theory and inversion models of underwater and atmospheric direct scattering spectrum are mainly introduced, as well as the existing measurement methods of direct scattering spectrum.
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Overview
Overview: Classical LiDAR based on energy detection was the first to be developed for environmental element detection, employing the lidar equation for the inversion of environmental parameters. In order to enhance the signal-to-noise ratio of the return signal and achieve accurate parameter inversion, high spectral resolution lidar(HSRL) has been identified as a viable approach. Both energy-detection-based lidar and HSRL utilized energy dimension information for remote sensing of environmental elements. However, the information captured solely in the energy dimension is limited. Environmental information is not only manifested in the energy dimension but also in the spectral dimension. Since the environmental information is not only manifested in the energy dimension but also in the spectral dimension, the characteristic information of the scattering spectrum, such as spectral energy, spectral line width, and spectral frequency shift, plays a very important role in the detection of multiple environmental elements. Based on the coupling relationship between the scattering spectral characteristic information and the medium environment, the inversion of multi-environmental elements can be directly carried out. By directly coupling the characteristic information of scattering spectra with the medium's environment parameters, direct scattering spectral lidar (DSSL) can realize the inversion of multiple environmental parameters. DSSL exhibits significant advantages in the detection of multiple environmental elements. However, with the demand for more detailed spectral information, the detection requirements of DSSL have also increased and primarily displayed in two aspects: 1) Spectral characteristic research: Utilizing spectral characteristic information for the detection of multiple environmental elements necessitates in-depth research on the relationship between underwater temperature, salinity, atmospheric temperature, pressure, and other environmental parameters with scattering spectra. This includes a thorough investigation of the spectral characteristics of direct scattering under various temperature, salinity, or pressure conditions, and the establishment of inversion models for multiple environmental elements based on the coupling relationship between direct scattering spectrum and environmental parameters. 2) Spectrum detection methods: As DSSL directly utilizes spectral dimension characteristic information, it places higher demands on the precise detection of scattering spectra. During spectral detection, attention should be paid to the system's sensitivity and accuracy, while considering real-time measurement continuity, vertical profile measurement, and the integrity of spectral detection. Acquiring more accurate scattering spectra is necessary to enhance the accuracy of the final detection results for multiple environmental elements. In this paper, we focus on the development of the inversion theory and various spectral detection methods for direct scattering spectral detection in underwater and atmospheric environments, providing an outlook on its future development.
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图 2 不同散射区域布里渊散射光谱图
Figure 2. Brioullin scattering spectrum in different scattering regions
图 3 典型瑞利布里渊散射光谱。 (a)海洋瑞利布里渊散射光谱；(b)大气瑞利布里渊散射光谱
Figure 3. Typical Rayleigh-Brillouin scattering spectrum: (a) In the ocean; (b) In the atmosphere
图 4 大气瑞利布里渊高斯拟合示意图
Figure 4. Schematic diagram of approximation Gaussian fitting for Rayleigh-Brillouin in the atmosphere
图 6 扫描F-P干涉仪测量水中布里渊散射实验装置图[51]
Figure 6. Experimental diagram of scanning F-P interferometer for measuring Brillouin scattering in water
图 7 使用扫描F-P干涉仪在水中测得的布里渊散射谱线图[51]
Figure 7. Brillouin scattering spectrum plot obtained using scanning F-P interferometer in water
图 8 采用扫描F-P干涉仪的大气激光雷达系统实验装置图[53]
Figure 8. Experimental diagram of atmospheric LiDAR system with scanning F-P interferometer
图 9 使用扫描F-P干涉仪在大气中测得的布里渊散射谱线与理论谱线对比图[53]。(a)最终的瑞利布里渊散射剖面;(b)测量值与Tenti S6模型之间的残差
Figure 9. Comparison of experimental and theoretical Brillouin scattering spectrum using scanning F-P interferometer in the atmosphere. (a) The final Rayleigh-Brillouin scattering profile;(b) Residuals between experimental measurements and Tenti S6 model
图 11 F-P标准具结合ICCD 进行光谱检测原理图
Figure 11. Schematic diagram of spectrum detection using F-P etalon combined with ICCD
图 13 实验原始光谱及处理后结果[57]。(a)实际温度20℃和实际盐度35‰下ICCD采集的干涉光谱图;(b)散射信号的二维光谱及其处理
Figure 13. Experimental raw spectra and processed results. (a) Interference spectrogram collected by ICCD with actual temperature of 20 ℃ and actual salinity of 35‰; (b) Two-dimensional spectra and processing of scattering signals
图 16 多边缘探测技术测风原理及测量结果图[69]。(a)基于F-P标准具四边双频技术的测风原理；(b)径向风速测量误差；(c)后向散射比测量误差和后向散射比
Figure 16. Principle and measurement results of multi-edge detection technique for wind measurement[69]. (a) Wind measurement principle based on F–P etalon quad-edge and dual-frequency technique; (b) Radial wind speed measurement error; (c) Backscatter ratio measurement error vs. backscatter ratio
图 18 DSSL利用双边缘技术反演温盐结果[60]。 (a) 反演温度误差随积分次数变化(5000次时误差小于0.1 °C)；(b) 反演盐度误差随积分次数变化(5000次时误差小于0.5%)
Figure 18. Temperature and salinity retrieval using double edge technique in DSSL[60]. (a) Variation of temperature retrieval error with integration iterations (Error is less than 0.1 °C at 5000 iterations); (b) Variation of salinity retrieval error with integration iterations (Error is less than 0.5% at 5000 iterations)
图 19 Fizeau干涉仪结合PMT阵列进行光谱检测测原理图
Figure 19. Schematic diagram of spectrum detection using the Fizeau interferometer combined with PMT array
图 21 德国宇航中心瑞利布里渊散射光谱和温度测量结果[62]。(a)瑞利布里渊散射光谱不同高度测量结果；(b)测量温度(红线)和无线电探空仪测量温度对比曲线
Figure 21. Rayleigh Brillouin scattering spectroscopy and temperature measurement results at DLR[62]. (a) Measurement results of Raman Brillouin scattering spectroscopy at different altitudes; (b) Comparison of measured temperature (red line) with radiosonde measured temperature
图 22 DSSL反演温压结果[50]。(a)反演温度(红线)和无线电探空仪测量温度对比曲线；(b)反演压强(红线)和无线电探空仪测量压强对比曲线
Figure 22. Temperature and pressure retrieval results based on DSSL[50]. (a) Comparison of retrieved temperature (red line) with radiosonde measured temperature; (b) Comparison of retrieved pressure (red line) with radiosonde measured pressure
图 24 VIPA进行光谱检测原理图。(a) VIPA色散几何图解；(b) VIPA干涉的特写图
Figure 24. Schematic diagram of spectrum detection using VIPA. (a) Illustration of the VIPA spectral dispersing geometry; (b) A close-up illustration of the interference geometry of the VIPA
图 26 在热水的自发冷却过程中获得的布里渊散射光谱图[65]。(a)不同时间所得布里渊光谱图；(b,c)在考虑分子碘吸收和不考虑分子碘吸收的情况下，得到的布里渊位移和线宽；(d-f)基于Stokes峰和反Stokes峰的布里渊位移的比较
Figure 26. Illustration of the Brillouin spectra acquired during a spontaneous cooling process of hot water[65]. (a) The acquired Brillouin spectra at different times; (b, c) The retrieved Brillouin shift and linewidth with and without consideration of the molecular iodine absorption; (d–f) Comparison between the Brillouin shift based on the Stokes and anti-Stokes peaks
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