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Guo Y X, Liang K, Xu Y R, et al. Multiple environmental elements laser remote sensing method based on direct scattering spectrum[J]. Opto-Electron Eng, 2024, 51(3): 240003. doi: 10.12086/oee.2024.240003
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Multiple environmental elements laser remote sensing method based on direct scattering spectrum
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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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References
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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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Guo Y X, Liang K, Xu Y R, et al. Multiple environmental elements laser remote sensing method based on direct scattering spectrum[J]. Opto-Electron Eng, 2024, 51(3): 240003. doi: 10.12086/oee.2024.240003
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Figure 1.
Schematic diagram of scattering spectrum
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Figure 2.
Brioullin scattering spectrum in different scattering regions
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Figure 3.
Typical Rayleigh-Brillouin scattering spectrum: (a) In the ocean; (b) In the atmosphere
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Figure 4.
Schematic diagram of approximation Gaussian fitting for Rayleigh-Brillouin in the atmosphere
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Figure 5.
Schematic diagram of F-P interferometer
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Figure 6.
Experimental diagram of scanning F-P interferometer for measuring Brillouin scattering in water
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Figure 7.
Brillouin scattering spectrum plot obtained using scanning F-P interferometer in water
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Figure 8.
Experimental diagram of atmospheric LiDAR system with scanning F-P interferometer
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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
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Figure 10.
Ubachs experimental results map[54]. (a) Correlation between volume viscosity
${\eta _{\rm{b}}}$ -
Figure 11.
Schematic diagram of spectrum detection using F-P etalon combined with ICCD
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Figure 12.
Brillouin scattering spectra obtained in seawater using F-P etalon combined with ICCD[56]
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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
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Figure 14.
Error distribution graphs[57]. (a) Measurement errors of frequency shift and line width; (b) Inversion errors of temperature and salinity
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Figure 15.
Schematic diagram of atmospheric edge technology[67]
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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
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Figure 17.
Double edge technique used in Brillouin scattering spectrum. (a) Principle of double edge filtering technique[59]; (b) Schematic diagram of the double edge underwater experimental system[60]
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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)
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Figure 19.
Schematic diagram of spectrum detection using the Fizeau interferometer combined with PMT array
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Figure 20.
Schematic diagram of the laser radar system using the Fizeau interferometer combined with PMT array[64]. (a) Schematic diagram of the underwater LiDAR system; (b) Schematic diagram of the atmospheric LiDAR System[63]
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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
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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
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Figure 23.
Results of wind velocity and aerosol detection potential[50]. (a) Retrieval results of wind speed variation within the altitude range of 4～9 km; (b) Retrieval results of aerosol extinction coefficient
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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
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Figure 25.
Experimental setup diagram combining the VIPA interferometer with a CCD camera[65]. (a) The schematics of the optical setup for the Brillouin spectroscopy and microscopy; (b) The VIPA spectrometer in greater details
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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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Figure 27.
Rayleigh brillouin spectroscopy measurement results[66]. (a) Spectra measured from 50~400 kPa; (b) Spectral profile image on the CCD camera at gas pressure of 400 kPa
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Figure 28.
Temperature results measured by rayleigh brillouin scattering spectrum[66]. (a) Retrieved temperature values (red triangle) and PT100 measured values (black star); (b) Error result
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