Analysis and Validation of the Signal-to-Noise Ratio for an Atmospheric Humidity Profiling Spectrometer Based on 1D-Imaging Spatial Heterodyne Spectroscopy
Sub-kilometer spatial resolution humidity profiles from the stratosphere to the mesosphere are essential for investigating the function of atmospheric water vapor in the global water and energy cycles as well as in radiation transport. The significant variations in atmospheric radiation at low altit...
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2025-05-01
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| author | Shaochun Xie Haiyan Luo Zhiwei Li Wei Jin Qiong Wu Mai Hu Yang Hong Wei Xiong |
| author_facet | Shaochun Xie Haiyan Luo Zhiwei Li Wei Jin Qiong Wu Mai Hu Yang Hong Wei Xiong |
| author_sort | Shaochun Xie |
| collection | DOAJ |
| description | Sub-kilometer spatial resolution humidity profiles from the stratosphere to the mesosphere are essential for investigating the function of atmospheric water vapor in the global water and energy cycles as well as in radiation transport. The significant variations in atmospheric radiation at low altitudes and the gradual changes at high altitudes pose challenges to the data acquisition and processing methods of limb imaging spectrometers that rely on atmospheric scattering and absorption mechanisms. In this paper, the effects of two binning techniques—interferogram binning and recovered spectrum binning—on improving the spectral signal-to-noise ratio (SNR) are examined through theoretical analysis and simulations, exemplified by a one-dimensional (1D) imaging spatial heterodyne spectrometer designed for measuring atmospheric humidity profiles. Rician random variables are employed to characterize the amplitude of the recovered spectral points under varying signal conditions, from which spectral SNR expressions are derived for both binning methods. The difference in both methods is evaluated through numerical simulations and experiments. Simulation results demonstrate that, with an integration time of 0.3 s and a spectral resolution of 0.03 nm, the input signal below 50 km is strong, with photon noise being the dominant factor, and both binning methods improve SNR proportionally to the square root of the number of binned rows. As the signal weakens above 50 km, additive noise gradually becomes dominant with increasing tangent altitude, and spectrum binning yields a higher SNR than interferogram binning. Experimental data obtained from a similar type of spectrometer further validate these simulation findings. The results indicate that spectrum binning provides greater advantages in improving the SNR for detecting water vapor in the mesosphere, paving the way for achieving a higher vertical resolution in subsequent retrievals. |
| format | Article |
| id | doaj-art-7bf7b6762a8b4f82b2d12aa1416cf36e |
| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-7bf7b6762a8b4f82b2d12aa1416cf36e2025-08-20T02:23:44ZengMDPI AGRemote Sensing2072-42922025-05-011711181010.3390/rs17111810Analysis and Validation of the Signal-to-Noise Ratio for an Atmospheric Humidity Profiling Spectrometer Based on 1D-Imaging Spatial Heterodyne SpectroscopyShaochun Xie0Haiyan Luo1Zhiwei Li2Wei Jin3Qiong Wu4Mai Hu5Yang Hong6Wei Xiong7Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, ChinaAnhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, ChinaAnhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, ChinaAnhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, ChinaAnhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, ChinaJianghuai Advance Technology Center, Hefei 230088, ChinaJianghuai Advance Technology Center, Hefei 230088, ChinaAnhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, ChinaSub-kilometer spatial resolution humidity profiles from the stratosphere to the mesosphere are essential for investigating the function of atmospheric water vapor in the global water and energy cycles as well as in radiation transport. The significant variations in atmospheric radiation at low altitudes and the gradual changes at high altitudes pose challenges to the data acquisition and processing methods of limb imaging spectrometers that rely on atmospheric scattering and absorption mechanisms. In this paper, the effects of two binning techniques—interferogram binning and recovered spectrum binning—on improving the spectral signal-to-noise ratio (SNR) are examined through theoretical analysis and simulations, exemplified by a one-dimensional (1D) imaging spatial heterodyne spectrometer designed for measuring atmospheric humidity profiles. Rician random variables are employed to characterize the amplitude of the recovered spectral points under varying signal conditions, from which spectral SNR expressions are derived for both binning methods. The difference in both methods is evaluated through numerical simulations and experiments. Simulation results demonstrate that, with an integration time of 0.3 s and a spectral resolution of 0.03 nm, the input signal below 50 km is strong, with photon noise being the dominant factor, and both binning methods improve SNR proportionally to the square root of the number of binned rows. As the signal weakens above 50 km, additive noise gradually becomes dominant with increasing tangent altitude, and spectrum binning yields a higher SNR than interferogram binning. Experimental data obtained from a similar type of spectrometer further validate these simulation findings. The results indicate that spectrum binning provides greater advantages in improving the SNR for detecting water vapor in the mesosphere, paving the way for achieving a higher vertical resolution in subsequent retrievals.https://www.mdpi.com/2072-4292/17/11/1810spatial heterodyne spectroscopyatmospheric humidity profiledata binningspectral signal-to-noise ratio |
| spellingShingle | Shaochun Xie Haiyan Luo Zhiwei Li Wei Jin Qiong Wu Mai Hu Yang Hong Wei Xiong Analysis and Validation of the Signal-to-Noise Ratio for an Atmospheric Humidity Profiling Spectrometer Based on 1D-Imaging Spatial Heterodyne Spectroscopy Remote Sensing spatial heterodyne spectroscopy atmospheric humidity profile data binning spectral signal-to-noise ratio |
| title | Analysis and Validation of the Signal-to-Noise Ratio for an Atmospheric Humidity Profiling Spectrometer Based on 1D-Imaging Spatial Heterodyne Spectroscopy |
| title_full | Analysis and Validation of the Signal-to-Noise Ratio for an Atmospheric Humidity Profiling Spectrometer Based on 1D-Imaging Spatial Heterodyne Spectroscopy |
| title_fullStr | Analysis and Validation of the Signal-to-Noise Ratio for an Atmospheric Humidity Profiling Spectrometer Based on 1D-Imaging Spatial Heterodyne Spectroscopy |
| title_full_unstemmed | Analysis and Validation of the Signal-to-Noise Ratio for an Atmospheric Humidity Profiling Spectrometer Based on 1D-Imaging Spatial Heterodyne Spectroscopy |
| title_short | Analysis and Validation of the Signal-to-Noise Ratio for an Atmospheric Humidity Profiling Spectrometer Based on 1D-Imaging Spatial Heterodyne Spectroscopy |
| title_sort | analysis and validation of the signal to noise ratio for an atmospheric humidity profiling spectrometer based on 1d imaging spatial heterodyne spectroscopy |
| topic | spatial heterodyne spectroscopy atmospheric humidity profile data binning spectral signal-to-noise ratio |
| url | https://www.mdpi.com/2072-4292/17/11/1810 |
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