# Turbulence: A Significant Role in Clear-Air Echoes of CINRAD/SA at Night

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## Abstract

**:**

## 1. Introduction

## 2. Concepts and Theory

#### 2.1. Dual-Polarization Radar Products

#### 2.2. Turbulence

#### 2.3. Bragg Scattering

#### 2.4. Biological Scattering

## 3. Instruments and Data

#### 3.1. Instruments

#### 3.2. Preprocessing

#### 3.2.1. Threshold

#### 3.2.2. Vertical Profiles

#### 3.2.3. Dual-Wavelength Ratio

## 4. Results

#### 4.1. Plan Position Indicator

#### 4.2. Time–Height Cross-Section

#### 4.3. Velocity Analysis

#### 4.4. Comparison of the S-Band and X-Band

## 5. Discussion

## 6. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

- Van Doren, B.M.; Horton, K.G. A continental system for forecasting bird migration. Science
**2018**, 361, 1115–1117. [Google Scholar] [CrossRef] [PubMed] - Bruderer, B. The study of bird migration by radar part 1: The technical basis. Naturwissenschaften
**1997**, 84, 1–8. [Google Scholar] [CrossRef] - Wilson, J.W.; Weckwerth, T.M.; Vivekanandan, J.; Wakimoto, R.M.; Russell, R.W. Boundary Layer Clear-Air Radar Echoes: Origin of Echoes and Accuracy of Derived Winds. J. Atmos. Ocean. Technol.
**1994**, 11, 1184–1206. [Google Scholar] [CrossRef] - Martin, W.J.; Shapiro, A. Discrimination of bird and insect radar echoes in clear air using high-resolution radars. J. Atmos. Ocean. Technol.
**2007**, 24, 1215–1230. [Google Scholar] [CrossRef] - Van den Broeke, M.S. Polarimetric Radar Observations of Biological Scatterers in Hurricanes Irene (2011) and Sandy (2012). J. Atmos. Ocean. Technol.
**2013**, 30, 2754–2767. [Google Scholar] [CrossRef][Green Version] - Westbrook, J.K.; Eyster, R.S.; Wolf, W.W. WSR-88D doppler radar detection of corn earworm moth migration. Int. J. Biometeorol.
**2014**, 58, 931–940. [Google Scholar] [CrossRef] [PubMed] - Zrnic, D.S.; Ryzhkov, A.V. Observations of insects and birds with a polarimetric radar. IEEE Trans. Geosci. Remote Sens.
**1998**, 36, 661–668. [Google Scholar] [CrossRef] - Ottersten, H. Atmospheric Structure and Radar Backscattering in Clear Air. Radio Sci.
**1969**, 4, 1179–1193. [Google Scholar] [CrossRef] - Kolmogorov, A.N.; Levin, V.; Hunt, J.C.R.; Phillips, O.M.; Williams, D. The local structure of turbulence in incompressible viscous fluid for very large Reynolds numbers. Rep. AS USSR
**1941**, 434, 9–13. [Google Scholar] [CrossRef] - Kolmogorov, A.N. A refinement of previous hypotheses concerning the local structure of turbulence in a viscous incompressible fluid at high Reynolds number. J. Fluid Mech.
**1962**, 13, 82–85. [Google Scholar] [CrossRef][Green Version] - Mandelbrot, B.B. Intermittent turbulence and fractal dimension: Kurtosis and the spectral exponent 5/3 + B. In Multifractals and 1/ƒ Noise: Wild Self-Affinity in Physics (1963–1976); Mandelbrot, B.B., Ed.; Springer: New York, NY, USA, 1976; pp. 389–415. [Google Scholar]
- Ringuet, E.; Rozé, C.; Gouesbet, G. Experimental observation of type-II intermittency in a hydrodynamic system. Phys. Rev. E
**1993**, 47, 1405–1407. [Google Scholar] [CrossRef] [PubMed] - Batchelor, G.K.; Townsend, A.A.; Jeffreys, H. The nature of turbulent motion at large wave-numbers. Proc. R. Soc. Lond. Ser. A Math. Phys. Sci.
**1949**, 199, 238–255. [Google Scholar] [CrossRef] - Pomeau, Y.; Manneville, P. Intermittent transition to turbulence in dissipative dynamical systems. Commun. Math. Phys.
**1980**, 74, 189–197. [Google Scholar] [CrossRef] - Siggia, E.D. Numerical study of small-scale intermittency in three-dimensional turbulence. J. Fluid Mech.
**1981**, 107, 375–406. [Google Scholar] [CrossRef][Green Version] - Paladin, G.; Vulpiani, A. Anomalous scaling laws in multifractal objects. Phys. Rep.
**1987**, 156, 147–225. [Google Scholar] [CrossRef] - Huang, Y.N.; Huang, Y.D. On the transition to turbulence in pipe flow. Phys. D Nonlinear Phenom.
**1989**, 37, 153–159. [Google Scholar] [CrossRef] - Meneveau, C.; Sreenivasan, K.R. Interface dimension in intermittent turbulence. Phys. Rev. A
**1990**, 41, 2246–2248. [Google Scholar] [CrossRef] - Vassilicos, J.C. Turbulence and intermittency. Nature
**1995**, 374, 408–409. [Google Scholar] [CrossRef] - Benzi, R.; Biferale, L. Intermittency in Turbulence. In Theories of Turbulence; Oberlack, M., Busse, F.H., Eds.; Springer: Vienna, Austria, 2002; pp. 1–76. [Google Scholar]
- Jiménez, J. Intermittency in Turbulence. In Encyclopedia of Mathematical Physics; Françoise, J.-P., Naber, G.L., Tsun, T.S., Eds.; Academic Press: Oxford, UK, 2006; pp. 144–151. [Google Scholar]
- Belen’kii, M.S. Effect of the stratosphere on star image motion. Opt. Lett.
**1995**, 20, 1359–1361. [Google Scholar] [CrossRef] - Korotkova, O.; Toselli, I. Non-Classic Atmospheric Optical Turbulence: Review. Appl. Sci.
**2021**, 11, 8487. [Google Scholar] [CrossRef] - Rao, C.; Jiang, W.; Ling, N. Spatial and temporal characterization of phase fluctuations in non-Kolmogorov atmospheric turbulence. J. Mod. Opt.
**2000**, 47, 1111–1126. [Google Scholar] [CrossRef] - Andrews, L.C. Free-space optical system performance for laser beam propagation through non-Kolmogorov turbulence. Opt. Eng.
**2008**, 47, 026003. [Google Scholar] [CrossRef][Green Version] - Li, Y.; Zhu, W.; Wu, X.; Rao, R. Equivalent refractive-index structure constant of non-Kolmogorov turbulence. Opt. Express
**2015**, 23, 23004–23012. [Google Scholar] [CrossRef] - Ruizhong, R.; Yujie, L. Light Propagation through Non-Kolmogorov-Type Atmospheric Turbulence and Its Effects on Optical Engineering. Acta Opt. Sin.
**2015**, 35, 0501003. [Google Scholar] [CrossRef] - Yang, H.; Fang, Z.; Li, C.; Deng, X.; Xing, K.; Xie, C. Atmospheric Optical Turbulence Profile Measurement and Model Improvement over Arid and Semi-arid regions. Atmos. Meas. Tech. Discuss.
**2021**, 2021, 1–14. [Google Scholar] [CrossRef] - Richardson, L.M.; Cunningham, J.G.; Zittel, W.D.; Lee, R.R.; Ice, R.L.; Melnikov, V.M.; Hoban, N.P.; Gebauer, J.G. Bragg Scatter Detection by the WSR-88D. Part I: Algorithm Development. J. Atmos. Ocean. Technol.
**2017**, 34, 465–478. [Google Scholar] [CrossRef] - Villars, F.; Weisskopf, V.F. The scattering of electromagnetic waves by turbulent atmospheric fluctuations. Phys. Rev.
**1954**, 94, 232–240. [Google Scholar] [CrossRef] - Stepanian, P.M.; Horton, K.G.; Melnikov, V.M.; Zrnic, D.S.; Gauthreaux, S.A. Dual-polarization radar products for biological applications. Ecosphere
**2016**, 7, 27. [Google Scholar] [CrossRef] - Park, H.S.; Ryzhkov, A.V.; Zrnić, D.S.; Kim, K.-E. The Hydrometeor Classification Algorithm for the Polarimetric WSR-88D: Description and Application to an MCS. Weather Forecast.
**2009**, 24, 730–748. [Google Scholar] [CrossRef][Green Version] - Kilambi, A.; Fabry, F.; Meunier, V. A Simple and Effective Method for Separating Meteorological from Nonmeteorological Targets Using Dual-Polarization Data. J. Atmos. Ocean. Technol.
**2018**, 35, 1415–1424. [Google Scholar] [CrossRef] - Koistinen, J. Bird migration patterns on weather radars. Phys. Chem. Earth Pt B-Hydrol. Ocean. Atmos.
**2000**, 25, 1185–1193. [Google Scholar] [CrossRef] - Hu, C.; Fang, L.; Wang, R.; Zhou, C.; Li, W.; Zhang, F.; Lang, T.; Long, T. Analysis of Insect RCS Characteristics. J. Electron. Inf. Technol.
**2020**, 42, 140–153. [Google Scholar] - Wang, C.; Wu, C.; Liu, L.; Liu, X.; Chen, C. Integrated Correction Algorithm for X Band Dual-Polarization Radar Reflectivity Based on CINRAD/SA Radar. Atmosphere
**2020**, 11, 119. [Google Scholar] [CrossRef][Green Version] - Chen, Y.; Zou, Q.; Han, J.; Cluckie, I. Cinrad data quality control and precipitation estimation. Proc. Inst. Civ. Eng.—Water Manag.
**2009**, 162, 95–105. [Google Scholar] [CrossRef] - Vignal, B.; Andrieu, H.; Creutin, J.D. Identification of Vertical Profiles of Reflectivity from Volume Scan Radar Data. J. Appl. Meteorol.
**1999**, 38, 1214–1228. [Google Scholar] [CrossRef] - Joss, J.; Lee, R. The Application of Radar Gauge Comparisons to Operational Precipitation Profile Corrections. J. Appl. Meteorol.
**1995**, 34, 2612–2630. [Google Scholar] [CrossRef] - Joss, J.; Waldvogel, A.; Collier, C.G. Precipitation Measurement and Hydrology. In Radar in Meteorology: Battan Memorial and 40th Anniversary Radar Meteorology Conference; Atlas, D., Ed.; American Meteorological Society: Boston, MA, USA, 1990; pp. 577–606. [Google Scholar]
- Cuihong, W.; Yufa, W.; Tao, W.; Hongxiang, J. Vertical Profile of Radar Echo and Its Deteermination Methods. J. Appl. Meteorol. Sci.
**2006**, 17, 232–239. [Google Scholar] - Melnikov, V.M.; Istok, M.J.; Westbrook, J.K. Asymmetric Radar Echo Patterns from Insects. J. Atmos. Ocean. Technol.
**2015**, 32, 659–674. [Google Scholar] [CrossRef] - Farisenkov, S.E.; Kolomenskiy, D.; Petrov, P.N.; Engels, T.; Lapina, N.A.; Lehmann, F.O.; Onishi, R.; Liu, H.; Polilov, A.A. Novel flight style and light wings boost flight performance of tiny beetles. Nature
**2022**, 602, 96–100. [Google Scholar] [CrossRef] - Xingfu, J. The Physiological and Genetic Characteristics of Migratory Behavior and Genetic Diversity, as Determined by AFLP in the Oriental Armyworm, Mythimna Separata (Walker). Ph.D. Thesis, Chinese Academy of Agricultural Sciences, Beijing, China, 2004. [Google Scholar]
- Holleman, I.; van Gasteren, H.; Bouten, W. Quality Assessment of Weather Radar Wind Profiles during Bird Migration. J. Atmos. Ocean. Technol.
**2008**, 25, 2188–2198. [Google Scholar] [CrossRef][Green Version] - Dokter, A.M.; Liechti, F.; Stark, H.; Delobbe, L.; Tabary, P.; Holleman, I. Bird migration flight altitudes studied by a network of operational weather radars. J. R. Soc. Interface
**2011**, 8, 30–43. [Google Scholar] [CrossRef] [PubMed][Green Version] - Pei, L.; Qiu, C. The assessment of velocity azimuth display technique of doppler weather radar. J. Trop. Meteorol.
**2013**, 29, 597–606. [Google Scholar] - Benedict, L.H.; Gould, R.D. Towards better uncertainty estimates for turbulence statistics. Exp. Fluids
**1996**, 22, 129–136. [Google Scholar] [CrossRef] - Moraghan, A.; Kim, J.; Yoon, S.-J. Density distributions of outflow-driven turbulence. Mon. Not. R. Astron. Soc. Lett.
**2013**, 432, L80–L84. [Google Scholar] [CrossRef][Green Version] - Cael, B.B.; Mashayek, A. Log-Skew-Normality of Ocean Turbulence. Phys. Rev. Lett.
**2021**, 126, 224502. [Google Scholar] [CrossRef] - Zhao, Y.; Zhao, X.; Wu, L.; Mu, T.; Yu, F.; Kearsley, L.; Liang, X.; Fu, J.; Hou, X.; Peng, P.; et al. A 30,000-km journey by Apus apus pekinensis tracks arid lands between northern China and south-western Africa. Mov. Ecol.
**2022**, 10, 29. [Google Scholar] [CrossRef] - Huang, X.; Zhao, Y.; Liu, Y. Using light-level geolocations to monitor incubation behaviour of a cavity-nesting bird Apus apus pekinensis. Avian Res.
**2021**, 12, 9. [Google Scholar] [CrossRef] - Li, L.; Wu, Z.S.; Lin, L.K.; Zhang, R.; Zhao, Z.W. Study on the Prediction of Troposcatter Transmission Loss. IEEE Trans. Antennas Propag.
**2016**, 64, 1071–1079. [Google Scholar] [CrossRef] - Zhang, M.G. Tropospheric Scatter Propagation; Publishing House of Electronics Industry: Beijing, China, 2004; Volume 10. [Google Scholar]
- Bullington, K. Reflections from an exponential atmosphere. Bell Syst. Tech. J.
**1963**, 42, 2849–2867. [Google Scholar] [CrossRef] - Zoumakis, N.M. On the relationship between the gradient and the bulk Richardson number for the atmospheric surface layer. Il Nuovo Cim. C
**1992**, 15, 111–114. [Google Scholar] [CrossRef] - Ren, G.; Liu, J.; Wan, J.; Li, F.; Guo, Y.; Yu, D. The analysis of turbulence intensity based on wind speed data in onshore wind farms. Renew. Energy
**2018**, 123, 756–766. [Google Scholar] [CrossRef] - Day, J.P.; Trolese, L.G. Propagation of Short Radio Waves over Desert Terrain. Proc. IRE
**1950**, 38, 165–175. [Google Scholar] [CrossRef] - Katzin, M.; Bauchman, R.W.; Binnian, W. 3- and 9-Centimeter Propagation in Low Ocean Ducts. Proc. IRE
**1947**, 35, 891–905. [Google Scholar] [CrossRef] - Melnikov, V.; Zrnić, D.S. Observations of Convective Thermals with Weather Radar. J. Atmos. Ocean. Technol.
**2017**, 34, 1585–1590. [Google Scholar] [CrossRef] - Melnikov, V.M.; Doviak, R.J.; Zrnić, D.S.; Stensrud, D.J. Structures of Bragg Scatter Observed with the Polarimetric WSR-88D. J. Atmos. Ocean. Technol.
**2013**, 30, 1253–1258. [Google Scholar] [CrossRef] - Richardson, L.M.; Zittel, W.D.; Lee, R.R.; Melnikov, V.M.; Ice, R.L.; Cunningham, J.G. Bragg Scatter Detection by the WSR-88D. Part II: Assessment of Z(DR) Bias Estimation. J. Atmos. Ocean. Technol.
**2017**, 34, 479–493. [Google Scholar] [CrossRef] - Banghoff, J.R.; Stensrud, D.J.; Kumjian, M.R. Convective Boundary Layer Depth Estimation from S-Band Dual-Polarization Radar. J. Atmos. Ocean. Technol.
**2018**, 35, 1723–1733. [Google Scholar] [CrossRef] - Stull, R.B. An Introduction to Boundary Layer Meteorology; Kluwer Academic: Dordrecht, The Netherlands, 1988. [Google Scholar]
- Hufford, G.L.; Kelley, H.L.; Sparkman, W.; Moore, R.K. Use of Real-Time Multisatellite and Radar Data to Support Forest Fire Management. Weather Forecast.
**1998**, 13, 592–605. [Google Scholar] [CrossRef] - Melnikov, V.M.; Zrnic, D.S.; Rabin, R.M.; Zhang, P. Radar polarimetric signatures of fire plumes in Oklahoma. Geophys. Res. Lett.
**2008**, 35, L14815. [Google Scholar] [CrossRef] - Zhang, G.; Doviak, R.; Palmer, R. Bistatic interferometry to measure clear air wind. In Proceedings of the 32nd Conference on Radar Meteorology, Albuquerque, NM, USA, 24–29 October 2005. [Google Scholar]

**Figure 1.**Distribution of radars (square signs and diamonds) and topography (coloring) of Beijing and its vicinity. The three square signs indicate the locations of the three X-POL radar sites (XFS, XSY, and XTZ). The diamond shows the location of the SDX site. The distance of each X-POL radar relative to the SDX site is labeled. The black dotted circle is the distance circle of the S-band with a 58 km radius, and the radius of the white dotted circles of the X-POL radar sites are 34 km, respectively. These circles show the detection zones of the CINRAD/SA and X-POLs where their minimum detectable reflectivity is less than −5 dBZ.

**Figure 2.**Azimuthal dependencies of the reflectivity factor for three species at the S-band (

**a**,

**c**) and X-band (

**b**,

**d**). As in (

**a**,

**b**), the elevation of the radar beam is 0.5°, which is the minimum elevation of VCP 21, and in (

**c**,

**d**), the elevation is 19.5°, which is the maximum elevation of VCP 21.

**Figure 3.**The simulated vertical profiles of the DWR between the S-band and X-band with the vertical height. The vertical profiles of turbulence fall in the grey-shaded area because of the different turbulence theories. The parameters of the three species are referenced from Table 1. All species are major agricultural pests in North China.

**Figure 4.**Radar products from the CINRAD/SA radar (SDX), Daxing, Beijing, on 2 May 2021 at 13:00 UTC for an elevation angle of 2.4°. The mapped domain is 75 km by 75 km.

**Figure 5.**(

**a**) An example of VAD data from the CINRAD/SA in Beijing. The line is the modeled radial velocities as a sine function of the azimuth, and the dots are the data of the Doppler velocity. The data samples are in the range of 30 km and the elevation of 2.4 deg. (

**b**) The residual error (the dot) is essentially less than 2 m/s. The root mean squared error is 1.278, and the adjusted R-square is 0.9924.

**Figure 6.**Combined reflectivity factor for 2.4 h from the CINRAD/SA, 2 May 2021. (

**a**–

**i**) Continuous observations from the CINRAD/SA at 12 min intervals from 11:12 to 13:36. The horizontal and vertical coordinates are, respectively, the ranges (km) in the west–east direction and the south–north direction.

**Figure 7.**Mean Doppler velocity for 2.4 h from the CINRAD/SA, 2 May 2021. (

**a**–

**i**) Continuous observations from the CINRAD/SA at 12 min intervals from 11:12 to 13:36. The horizontal and vertical coordinates are, respectively, the ranges (km) in the west–east direction and the south–north direction.

**Figure 8.**Range–height cross-section of $Z$ following the azimuthal direction (azimuth: 225°). (

**a**–

**f**) Continuous observations from the CINRAD/SA at 6 min intervals from 11:48 to 12:28, 2 May 2021. The horizontal axis is the range (km) along the wind direction, and the vertical axis is the height (km).

**Figure 9.**Time–height cross-section of radar products for 24 h from the CINRAD/SA at Daxing, Beijing, 2 May 2021. The fill color is $Z$ (unit: dBZ); the black isopleths are valid data proportions; and the white isopleths are ${Z}_{DR}$ (unit: dB). The times of sunset and sunrise were 11:08 and 21:13, UTC, respectively.

**Figure 10.**Vertical wind profiles measured using the Windcube 100 s on 2 May 2021. The positive and negative values represent the vertical upward and downward wind speeds (unit: m/s), respectively.

**Figure 11.**Doppler velocity fields (

**a**–

**c**) and the fields which were obtained from the VAD analysis (

**d**–

**f**). The elevation angles of (

**a**–

**c**) are 0.5°, 1.5°, and 2.4°, respectively, the same as (

**d**–

**f**). Except for some error points and point targets, the velocity fields (

**d**–

**f**) which were obtained from the VAD analysis are similar to the Doppler velocity (

**a**–

**c**).

**Figure 12.**(

**a**) Histogram of the residual error of the VAD analysis at different altitudes. The volume of the radar beam is used to calculate the ordinate. (

**b**) The profiles of the mean wind speed during 12:30 to 13:30 (UTC) on 2 May 2021.

**Figure 13.**Time–height cross-section of $Z$ (the fill color) and the valid data proportions (the black isopleths) for 24 h from the X-band radar at FS, Beijing, 2 May 2021.

**Figure 14.**The DWR between the values of Figure 8 and Figure 12. The time–height cross-section and the histogram of the DWR at nighttime are exhibited in (

**a**) and (

**b**), respectively. The black isopleth in (

**a**) is the proportion of the X-band valid data. The bar chart (

**b**) represents the normalized frequency distribution of the DWR from sunset to sunrise, and the data in the histogram from 19 dB to 25 dB occupy 80%.

**Figure 15.**Histograms of the radar products of the CINRAD/SA (S-band) and X-POL (X-band), respectively, on 2 May 2021 at 13:00 UTC for a height ranging between 300 m and 1.2 km.

**Figure 16.**Turbulence intensities (

**a**) and Richardson number (

**b**) for 20 h at Daxing, Beijing, 2 May 2021. The black isopleth is the time–height cross-section of $Z$ (unit: dBZ), which is the same as the fill color in Figure 6.

**Figure 17.**Time–height cross-section of the Z values (

**a**) from 22:30 to 24:00, and the vertical profiles of the radar products (

**b**,

**c**) and rawinsonde products (

**d**,

**e**) at 23:15 UTC on 2 May 2021. The profiles of (

**d**,

**e**) are measured by rawinsondes. The estimated entrainment layer is based on the maximum vertical gradient in each variable (orange line).

**Table 1.**Parameters of several insects and their RCSs at different wavelengths. The biometric data were provided by the Chinese Academy of Agricultural Sciences from captured insects in North China. The RCSs were simulated by FEKO simulation software using the prolate spheroid model of a spinal cord dielectric.

Species | Average Weight (mg) | Average Length (mm) | Average Width (mm) | RCS of S-Band (dBsm) | RCS of X-Band (dBsm) |
---|---|---|---|---|---|

Conogethes punctiferalis, Hawaiian beet webworm, Athetis lepigone | 22.1 | 13.0 | 3.2 | −52.5 | −25.0 |

Cotton bollworms, Plusia agnata | 114.8 | 16.7 | 5.4 | −39.8 | −34.2 |

Armyworms, Black cutworms, Sprodoptera litura | 145.4 | 19.0 | 5.8 | −36.2 | −33.8 |

Parameter | CINRAD/SA Radar | X-POL Radars |
---|---|---|

Frequency | 2700–3000 MHz | 9300–9500 MHz |

Antenna cover diameter | 11.9 m | ≥4 m |

Polarization | Linear H and V | Linear H and V |

Volume coverage patterns | VCP 21 | VCP 21 |

Time of VCP 21 | 6 min | 3 min |

Range resolution | 250 m | 75 m |

Minimum detectable reflectivity | −7.5 dBZ @ 50 km | 5 dBZ @ 60 km |

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## Share and Cite

**MDPI and ACS Style**

Teng, Y.; Li, T.; Ma, S.; Chen, H. Turbulence: A Significant Role in Clear-Air Echoes of CINRAD/SA at Night. *Remote Sens.* **2023**, *15*, 1781.
https://doi.org/10.3390/rs15071781

**AMA Style**

Teng Y, Li T, Ma S, Chen H. Turbulence: A Significant Role in Clear-Air Echoes of CINRAD/SA at Night. *Remote Sensing*. 2023; 15(7):1781.
https://doi.org/10.3390/rs15071781

**Chicago/Turabian Style**

Teng, Yupeng, Tianyan Li, Shuqing Ma, and Hongbin Chen. 2023. "Turbulence: A Significant Role in Clear-Air Echoes of CINRAD/SA at Night" *Remote Sensing* 15, no. 7: 1781.
https://doi.org/10.3390/rs15071781