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Future of Lunar Exploration
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Future of Lunar Exploration

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Satellite Missions for Earth and Planetary Exploration".

Deadline for manuscript submissions: closed (15 December 2024) | Viewed by 40987

Special Issue Editors

Dr. Jianqing Feng

E-Mail Website
Guest Editor
Planetary Science Institute, Tucson, AZ, USA
Interests: lunar regolith properties and shallow structure
Prof. Dr. Jianzhong Liu

E-Mail Website
Guest Editor
Institute of Geochemistry, Chinese Academy of Sciences, Guiyang, China
Interests: lunar and deep space exploration

Special Issue Information

Dear Colleagues,

The Moon has captivated humans since we first set eyes on it as the most prominent object in the night sky. The mysteries of the origin and evolution of the Moon continue to attract the interest and excitement of scientists and engineers worldwide. Since the first spacecraft, Luna 2, reached the lunar surface in 1959, humans have conducted more than 100 lunar exploration missions, culminating when Apollo astronauts stepped on the Moon in 1969–1972. In the 21st century, more probes with new detection technology have been deployed, including SMART-1; SELENE; Chandrayaan-1 and Chandrayaan-2; LCROSS; LRO; GRAIL; LADEE; and CE-1, CE-2, CE-3, CE-4, and CE-5, providing new insight into lunar science. In these missions, remote sensing is the most critical detection method, such as optical image, multiple-wavelength spectroscopy, passive and active microwave, gamma-ray, X-ray, neutron, etc. In the upcoming decades, lunar exploration will usher in new development. Represented by the Artemis program proposed by NASA of the United States, plans for crewed flights followed by moonbases were declared by the US, Russia, ESA, China, Japan, and India.

For this Special issue, “Future of Lunar Exploration”, we are inviting contributions on new findings in the field of lunar science, covering methods and applications, as well as overview papers. The topics include but are not limited to analysis of data from current or past explore missions, instrument concepts for planned or future missions, modeling of the remote sensing observations of the lunar surface or interior, laboratory analysis of returned samples, and Earth-based observation of the Moon.

Dr. Jianqing Feng
Prof. Dr. Jianzhong Liu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • lunar exploration
  • lunar geology
  • satellite remote sensing
  • data processing and interpretation
  • numerical modeling
  • sample analysis
  • earth-based observation

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Published Papers (13 papers)

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17 pages, 7157 KiB  
Article
The Geological Investigation of the Lunar Reiner Gamma Magnetic Anomaly Region
by Junhao Hu, Jingwen Liu, Jianzhong Liu, Jiayin Deng, Sheng Zhang, Danhong Lei, Xuejin Zeng and Weidong Huang
Remote Sens. 2024, 16(22), 4153; https://doi.org/10.3390/rs16224153 - 7 Nov 2024
Viewed by 667
Abstract
Reiner Gamma is a potential target for low-orbiting spacecraft or even surface-landed missions in the near future. Unfortunately, thus far, no comprehensive low-altitude (below 20 km) or surface measurements of the magnetic field, magnetic source and plasma environment have been made post-Apollo to [...] Read more.
Reiner Gamma is a potential target for low-orbiting spacecraft or even surface-landed missions in the near future. Unfortunately, thus far, no comprehensive low-altitude (below 20 km) or surface measurements of the magnetic field, magnetic source and plasma environment have been made post-Apollo to complement and complete our understanding of the solar wind interaction with lunar magnetic anomalies and swirl formation. Acquiring the detailed geological knowledge of the Reiner Gamma region is significant for the above scientific targets. In this study, the following research work in the lunar Reiner Gamma magnetic anomaly region was carried out for the regional geological investigation: (1) topographic and geomorphologic analysis; (2) element, mineral, and sequence analysis; and (3) a 1:10,000 regional geological map analysis. Our work helps define measurement requirements for possible future low-orbiting or surface-landed missions to the Reiner Gamma area or similarly magnetized regions of the lunar surface. Full article
(This article belongs to the Special Issue Future of Lunar Exploration)
►▼ Show Figures

Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>The background maps in Reiner Gamma region. (<b>a</b>–<b>d</b>) represent TOC, DEM, Slop, and Rock abundance in Reiner Gamma region, respectively.</p> Full article ">Figure 2
<p>The major element compositions in the Reiner Gamma region. (<b>a</b>–<b>f</b>) represent the MgO, FeO, Al<sub>2</sub>O<sub>3</sub>, CaO, TiO<sub>2</sub>, and SMFe contents in Reiner Gamma region, respectively.</p> Full article ">Figure 3
<p>The major mineral compositions in the Reiner Gamma region. (<b>a</b>–<b>d</b>) represent the olivine, orthopyroxene, clinopyroxene, and plagioclase contents in Reiner Gamma region, respectively.</p> Full article ">Figure 4
<p>Topographic and geomorphological parameters in the target of the Reiner Gamma region. (<b>a</b>,<b>b</b>) represent NAC and DEM maps in the target of the Reiner Gamma region. (<b>c</b>–<b>f</b>) represent the slop, roughness, rock abundance, and basic landforms in the target of the Reiner Gamma region, respectively.</p> Full article ">Figure 5
<p>The 1:100,00 geologic map of the target in the Reiner Gamma region. The “AB” represents the section line which traverses the major geological tectonics and the main magnetic anomaly units of this region (600 dpi in <a href="https://zenodo.org/uploads/14003443" target="_blank">https://zenodo.org/uploads/14003443</a>, accessed on 28 October 2024).</p> Full article ">Figure 6
<p>The stratigraphic sequence of the target in the Reiner Gamma region.</p> Full article ">
20 pages, 5498 KiB  
Article
Terahertz Emission Modeling of Lunar Regolith
by Suyun Wang
Remote Sens. 2024, 16(21), 4037; https://doi.org/10.3390/rs16214037 - 30 Oct 2024
Viewed by 628
Abstract
We investigate the terahertz (THz) scattering and emission properties of lunar regolith by modeling it as a random medium with rough top and bottom boundaries and a host medium situated beneath. The total scattering and emission arise from three sources: the rough boundaries, [...] Read more.
We investigate the terahertz (THz) scattering and emission properties of lunar regolith by modeling it as a random medium with rough top and bottom boundaries and a host medium situated beneath. The total scattering and emission arise from three sources: the rough boundaries, the volume, and the interactions between the boundaries and the volume. To account for these sources, we model their respective phase matrices and apply the matrix doubling approach to couple these phase matrices to compute the total emission. The model is then used to explore insights into lunar regolith scattering and emission processes. The simulations reveal that surface roughness is the primary contributor to total scattering, while dielectric contrasts between the volume and the boundaries dominate total emission. The THz emissivity is highly sensitive to the regolith dielectric constant, particularly its imaginary part, making it a promising alternative for identifying previously undetected water ice in the lunar polar regions. The THz emissivity model developed in this study can be readily applied to invert the surface parameters of lunar regolith using THz observations. Full article
(This article belongs to the Special Issue Future of Lunar Exploration)
►▼ Show Figures

Figure 1

Figure 1
<p>Schematic representation of emission from lunar regolith. The free-space and host medium are extended to half-space.</p> Full article ">Figure 2
<p>(<b>a</b>) Normalized correlation function and (<b>b</b>) corresponding power spectrum.</p> Full article ">Figure 3
<p>Dielectric profiles of lunar regolith. (<b>a</b>) Real part of the permittivity. (<b>b</b>) Imaginary part of the permittivity.</p> Full article ">Figure 4
<p>The minimum, average, and maximum temperature profiles of the lunar regolith for the thermal model at latitude 80 °C.</p> Full article ">Figure 5
<p>Angular scattering response to the regolith layer parameters with top and bottom boundaries.</p> Full article ">Figure 6
<p>Angular scattering response to the regolith layer parameters with top and bottom boundaries.</p> Full article ">Figure 7
<p>Effects of the real part of the layer dielectric constant on emissivity through a regolith layer with top and bottom rough boundaries. (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mi>ϵ</mi> <mi>s</mi> </msub> <mo>=</mo> <mn>1.1</mn> <mo>,</mo> <mn>1.3</mn> <mo>,</mo> <mn>1.5</mn> <mo>,</mo> <mn>2.0</mn> </mrow> </semantics></math> and (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mi>ϵ</mi> <mi>s</mi> </msub> <mo>=</mo> <mn>2.5</mn> <mo>,</mo> <mn>3.0</mn> <mo>,</mo> <mn>3.5</mn> <mo>,</mo> <mn>4.0</mn> <mo>.</mo> </mrow> </semantics></math></p> Full article ">Figure 8
<p>Comparison of emissivity from lunar regolith with different surface RMS height variations at 480 GHz using exponential and Gaussian correlation functions. (<b>a</b>) Exponential and (<b>b</b>) Gaussian.</p> Full article ">Figure 9
<p>Exponential and Gaussian emission from a lunar regolith with different correlation length variations. (<b>a</b>) Exponential and (<b>b</b>) Gaussian.</p> Full article ">Figure 10
<p>Effects of optical depth <math display="inline"><semantics> <mi>τ</mi> </semantics></math> on scattering.</p> Full article ">Figure 11
<p>Effects of albedo <math display="inline"><semantics> <msub> <mi>ω</mi> <mn>0</mn> </msub> </semantics></math> on scattering.</p> Full article ">Figure 12
<p>Effects of optical depth on emissivity through a regolith layer with top and bottom rough boundaries. (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mi>ω</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>0.1</mn> </mrow> </semantics></math> and (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mi>ω</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>0.2</mn> </mrow> </semantics></math>.</p> Full article ">Figure 13
<p>Effects of albedo on emissivity through a regolith layer with top and bottom rough boundaries. (<b>a</b>) Exponential and (<b>b</b>) Gaussian.</p> Full article ">Figure A1
<p>Backward and forward scattering due to an inhomogeneous layer. (<b>a</b>) Downward incidence. (<b>b</b>) Upward incidence.</p> Full article ">Figure A2
<p>Scattering process due to two adjacent inhomogeneous layers.</p> Full article ">Figure A3
<p>Multiple scattering processes at the interface between the inhomogeneous layer and host medium.</p> Full article ">
16 pages, 3710 KiB  
Article
Experimental Analysis of Terahertz Wave Scattering Characteristics of Simulated Lunar Regolith Surface
by Suyun Wang and Kazuma Hiramatsu
Remote Sens. 2024, 16(20), 3819; https://doi.org/10.3390/rs16203819 - 14 Oct 2024
Cited by 1 | Viewed by 819
Abstract
This study investigates terahertz (THz) wave scattering from a simulated lunar regolith surface, with a focus on the Brewster feature, backscattering, and bistatic scattering within the 325 to 500 GHz range. We employed a generalized power-law spectrum to characterize surface roughness and fabricated [...] Read more.
This study investigates terahertz (THz) wave scattering from a simulated lunar regolith surface, with a focus on the Brewster feature, backscattering, and bistatic scattering within the 325 to 500 GHz range. We employed a generalized power-law spectrum to characterize surface roughness and fabricated Gaussian correlated surfaces from Durable Resin V2 using 3D printing technology. The complex dielectric permittivity of these materials was determined through THz time-domain spectroscopy (THz-TDS). Our experimental setup comprised a vector network analyzer (VNA) equipped with dual waveguide frequency extenders for the WR-2.2 band, transmitter and receiver modules, polarizing components, and a scattering chamber. We systematically analyzed the effects of root-mean-square (RMS) height, correlation length, dielectric constant, frequency, polarization, and observation angle on THz scattering. The findings highlight the significant impact of surface roughness on the Brewster angle shift, backscattering, and bistatic scattering. These insights are crucial for refining theoretical models and developing algorithms to retrieve physical parameters for lunar and other celestial explorations. Full article
(This article belongs to the Special Issue Future of Lunar Exploration)
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Figure 1

Figure 1
<p>The geometry of wave scattering from rough surface.</p> Full article ">Figure 2
<p>The rough surface samples are designed with specified RMS heights and correlation lengths.</p> Full article ">Figure 3
<p>The measured dielectric constant of the material by THz-TDS.</p> Full article ">Figure 4
<p>The roughness validation of one selected rough surface with an RMS height of 0.8<math display="inline"><semantics> <mi>λ</mi> </semantics></math> and a correlation length of 2<math display="inline"><semantics> <mi>λ</mi> </semantics></math>.</p> Full article ">Figure 5
<p>The experiment configuration.</p> Full article ">Figure 6
<p>The polarizer consists of three reflectors.</p> Full article ">Figure 7
<p>The comparison between the simulated and experimental HH and VV reflections from a flat surface with a dielectric constant of <math display="inline"><semantics> <mrow> <mn>2.597</mn> <mo>+</mo> <mi>j</mi> <mn>0.165</mn> </mrow> </semantics></math>.</p> Full article ">Figure 8
<p>The frequency effect on THz scattering from rough surface.</p> Full article ">Figure 9
<p>The correlation length effect on THz scattering from rough surface. (<b>a</b>) <math display="inline"><semantics> <mi>σ</mi> </semantics></math> = 0.8<math display="inline"><semantics> <mi>λ</mi> </semantics></math> (<b>b</b>) <math display="inline"><semantics> <mi>σ</mi> </semantics></math> = 0.1<math display="inline"><semantics> <mi>λ</mi> </semantics></math>.</p> Full article ">Figure 10
<p>The RMS height effect on THz scattering from rough surface. (<b>a</b>) <span class="html-italic">l</span> = 2<math display="inline"><semantics> <mi>λ</mi> </semantics></math> (<b>b</b>) <span class="html-italic">l</span> = 0.4<math display="inline"><semantics> <mi>λ</mi> </semantics></math>.</p> Full article ">Figure 11
<p>Comparison of bistatic scattering from flat and rough surfaces with RMS heights of 0.5<math display="inline"><semantics> <mi>λ</mi> </semantics></math> and 0.8<math display="inline"><semantics> <mi>λ</mi> </semantics></math> and a fixed correlation length of 2<math display="inline"><semantics> <mi>λ</mi> </semantics></math> at incident angles of 30°, 45°, and 60° for both HH and VV polarizations.</p> Full article ">Figure 12
<p>Comparison of bistatic scattering from flat and rough surfaces with RMS heights of 0.1<math display="inline"><semantics> <mi>λ</mi> </semantics></math> and 0.08<math display="inline"><semantics> <mi>λ</mi> </semantics></math> and a fixed correlation length of 0.4<math display="inline"><semantics> <mi>λ</mi> </semantics></math> at incident angles of 30°, 45°, and 60° for both HH and VV polarizations.</p> Full article ">Figure 13
<p>Comparison of bistatic scattering from the flat surface and rough surface with different correlation lengths of 2<math display="inline"><semantics> <mi>λ</mi> </semantics></math>, 4<math display="inline"><semantics> <mi>λ</mi> </semantics></math> and 6<math display="inline"><semantics> <mi>λ</mi> </semantics></math> and a fixed RMS height of 0.8<math display="inline"><semantics> <mi>λ</mi> </semantics></math> at the incident angle of 30°, 45° and 60° for HH and VV polarizations.</p> Full article ">Figure 14
<p>Comparison of bistatic scattering from rough surfaces with correlation lengths of 1.5<math display="inline"><semantics> <mi>λ</mi> </semantics></math>, 1<math display="inline"><semantics> <mi>λ</mi> </semantics></math>, and 0.4<math display="inline"><semantics> <mi>λ</mi> </semantics></math> and a fixed RMS height of 0.1<math display="inline"><semantics> <mi>λ</mi> </semantics></math> at incident angles of 30°, 45°, and 60° for both HH and VV polarizations.</p> Full article ">Figure 15
<p>Comparison of three different incident angles 30°, 45°, and 60° for HH and VV polarizations from a Gaussian correlated surface of <span class="html-italic">l</span> = 1.0 <math display="inline"><semantics> <mi>λ</mi> </semantics></math>, <math display="inline"><semantics> <mi>σ</mi> </semantics></math> = 0.1 <math display="inline"><semantics> <mi>λ</mi> </semantics></math>. ((<b>left</b>): VV polarization, (<b>right</b>): HH polarization).</p> Full article ">Figure 16
<p>Comparison of three different incident angles 30°, 45°, and 60° for HH and VV polarizations from a Gaussian correlated surface of <span class="html-italic">l</span> = 2.0 <math display="inline"><semantics> <mi>λ</mi> </semantics></math>, <math display="inline"><semantics> <mi>σ</mi> </semantics></math> = 0.5 <math display="inline"><semantics> <mi>λ</mi> </semantics></math>. ((<b>left</b>): VV polarization, (<b>right</b>): HH polarization).</p> Full article ">
21 pages, 9414 KiB  
Article
Analysis of the Effect of Tilted Corner Cube Reflector Arrays on Lunar Laser Ranging
by Jin Cao, Rufeng Tang, Kai Huang, Zhulian Li, Yongzhang Yang, Kai Huang, Jintao Li and Yuqiang Li
Remote Sens. 2024, 16(16), 3030; https://doi.org/10.3390/rs16163030 - 18 Aug 2024
Viewed by 972
Abstract
This paper primarily investigates the effect of the tilt of corner cube reflector (CCR) arrays on lunar laser ranging (LLR). A mathematical model was established to study the random errors caused by the tilt of the CCR arrays. The study found that, ideally, [...] Read more.
This paper primarily investigates the effect of the tilt of corner cube reflector (CCR) arrays on lunar laser ranging (LLR). A mathematical model was established to study the random errors caused by the tilt of the CCR arrays. The study found that, ideally, when the laser ranging pulse width is 10 picoseconds or less, it is possible to distinguish from which specific corner cubes within the CCR array each peak in the echo signal originates. Consequently, partial data from the echo can be extracted for signal processing, significantly reducing random errors and improving the single-shot precision of LLR. The distance obtained by extracting part of the echo can be reduced to the center position of the array, thereby providing multiple higher-precision ranging results from each measurement. This not only improves the precision of LLR but also increases the data volume. A simulation experiment based on the 1.2 m laser ranging system at Yunnan Observatories was conducted. By extracting one peak for signal processing, the single-shot precision improved from 32.24 mm to 2.52 mm, validating the theoretical analysis results. Finally, an experimental laser ranging system based on a 53 cm binocular telescope system was established for ground experiments. The experimental results indicated that the echo signal could identify the tilt state of the CCR array. By extracting the peak returned by the central CCR for signal processing, the ranging precision was greatly improved. Through theoretical analyses, simulation experiments, and ground experiments, a solution to reduce the random errors caused by the tilt of the CCR array was provided. This offers an approach to enhance the single-shot precision of future LLR and provides a reference for upgrading ground-based equipment at future laser ranging stations. Full article
(This article belongs to the Special Issue Future of Lunar Exploration)
►▼ Show Figures

Figure 1

Figure 1
<p>CCR arrays. The figure displays all the CCR arrays on the lunar surface, along with some additional details. (Source: adapted from an image search result for “lunar corner cube reflector” on Bing, <a href="https://bing.com/" target="_blank">https://bing.com/</a>, accessed on 5 May 2024).</p> Full article ">Figure 2
<p>The CCR array sites on the Moon. (Source: adapted from an image search result for “lunar corner cube reflector” on Bing, <a href="https://bing.com/" target="_blank">https://bing.com/</a>, accessed on 5 May 2024).</p> Full article ">Figure 3
<p>Lunar libration amplitude (1 January 2000–31 December 2009. 10 years).</p> Full article ">Figure 4
<p>Lunar libration amplitude (1 January 2000–31 December 2000. 1 year).</p> Full article ">Figure 5
<p>Schematic diagram of a tilted CCR array with laser incidence.</p> Full article ">Figure 6
<p>Schematic diagram of the Apollo 11 and 14 LLR reflector arrays (d = 46 mm, <math display="inline"><semantics> <mi>ϕ</mi> </semantics></math> = 38 mm).</p> Full article ">Figure 7
<p>Envelope of different laser pulses due to the tilt of the CCR array (Apollo 11 and 14).</p> Full article ">Figure 8
<p>Range echo envelopes of the CCR arrays (Apollo 11 and 14) at different tilt angles.</p> Full article ">Figure 9
<p>Echo plot for the laser ranging simulation of the Apollo 11 CCR array.</p> Full article ">Figure 10
<p>A schematic diagram of the local experiment.</p> Full article ">Figure 11
<p>The experimental CCR array (the left image shows the 6 × 6 array of CCRs used in the experiments, the middle image depicts a single CCR, and the right image displays the manually adjustable tilt table that is capable of adjusting angles in two directions).</p> Full article ">Figure 12
<p>The experimental CCR array fixed on the exterior facade of the iron tower.</p> Full article ">Figure 13
<p>The optical system of the Yunnan Observatories’ 53 cm binocular telescope.</p> Full article ">Figure 14
<p>Experimental procedure (<b>left</b>: photo of adjusting the telescope direction and the size of the incident spot; <b>middle</b>: plane mirror attached to the array surface for adjusting the array tilt angle; <b>right</b>: photo of the incident laser.)</p> Full article ">Figure 15
<p>Laser vertically incident echos for different numbers of reflectors.</p> Full article ">Figure 16
<p>Echoes of the experimental CCR array at different tilt angles.</p> Full article ">Figure 17
<p>The peak value of the echo histogram changes with the tilt angle of the CCR array.</p> Full article ">Figure 18
<p>The residual echoes and their histograms at different tilt angles for the experimental CCR array using two columns.</p> Full article ">Figure 19
<p>The experimental results for the CCR array with three columns.</p> Full article ">Figure 20
<p>The experimental results for the CCR array with three columns.</p> Full article ">
23 pages, 33239 KiB  
Article
Lunar Surface Resource Exploration: Tracing Lithium, 7 Li and Black Ice Using Spectral Libraries and Apollo Mission Samples
by Susana del Carmen Fernández, Fernando Alberquilla, Julia María Fernández, Enrique Díez, Javier Rodríguez, Rubén Muñiz, Javier F. Calleja, Francisco Javier de Cos and Jesús Martínez-Frías
Remote Sens. 2024, 16(7), 1306; https://doi.org/10.3390/rs16071306 - 8 Apr 2024
Viewed by 1858
Abstract
This is an exercise to explore the concentration of lithium, lithium-7 isotope and the possible presence of black dirty ice on the lunar surface using spectral data obtained from the Clementine mission. The main interest in tracing the lithium and presence of dark [...] Read more.
This is an exercise to explore the concentration of lithium, lithium-7 isotope and the possible presence of black dirty ice on the lunar surface using spectral data obtained from the Clementine mission. The main interest in tracing the lithium and presence of dark ice on the lunar surface is closely related to future human settlement missions on the moon. We investigate the distribution of lithium and 7 Li isotope on the lunar surface by employing spectral data from the Clementine images. We utilized visible (VIS–NIR) imagery at wavelengths of 450, 750, 900, 950 and 1000 nm, along with near-infrared (NIR–SWIR) at 1100, 1250, 1500, 2000, 2600 and 2780 nm, encompassing 11 bands in total. This dataset offers a comprehensive coverage of about 80% of the lunar surface, with resolutions ranging from 100 to 500 m, spanning latitudes from 80°S to 80°N. In order to extract quantitative abundance of lithium, ground-truth sites were used to calibrate the Clementine images. Samples (specifically, 12045, 15058, 15475, 15555, 62255, 70035, 74220 and 75075) returned from Apollo missions 12, 15, 16 and 17 have been correlated to the Clementine VIS–NIR bands and five spectral ratios. The five spectral ratios calculated synthesize the main spectral features of sample spectra that were grouped by their lithium and 7 Li content using Principal Component Analysis. The ratios spectrally characterize substrates of anorthosite, silica-rich basalts, olivine-rich basalts, high-Ti mare basalts and Orange and Glasses soils. Our findings reveal a strong linear correlation between the spectral parameters and the lithium content in the eight Apollo samples. With the values of the 11 Clementine bands and the 5 spectral ratios, we performed linear regression models to estimate the concentration of lithium and 7 Li. Also, we calculated Digital Terrain Models (Altitude, Slope, Aspect, DirectInsolation and WindExposition) from LOLA-DTM to discover relations between relief and spatial distribution of the extended models of lithium and 7 Li. The analysis was conducted in a mask polygon around the Apollo 15 landing site. This analysis seeks to uncover potential 7 Li enrichment through spallation processes, influenced by varying exposure to solar wind. To explore the possibility of finding ice mixed with regolith (often referred to as `black ice’), we extended results to the entire Clementine coverage spectral indices, calculated with a library (350–2500 nm) of ice samples contaminated with various concentrations of volcanic particles. Full article
(This article belongs to the Special Issue Future of Lunar Exploration)
►▼ Show Figures

Figure 1

Figure 1
<p>Cartographic expression of the Wind Exposition Index. The areas in dark grey represent those less exposed to solar wind, while the areas in light grey represent those experiencing a greater impact from solar wind.</p> Full article ">Figure 2
<p>Training area (around the Apollo 15 landing site) and point population used to test the influence of relief variables in lithium distribution.</p> Full article ">Figure 3
<p>On the left, taking the spectral library (VIS–NIR) over Johnson Glacier; in the middle, the Black Glacier of Deception Inland; on the right, front of Johnson Glacier, covered with lapilli.</p> Full article ">Figure 4
<p>On the left, extraction and characterization in the Juan Carlos I Spanish Scientific Base in Livingstone Island of impurities from a snow sample of the Johnsons Glacier. On the right, we can see the ice rendered dirty by us with lapilli. In the middle, the ADS spectroradiometer taking spectra of dirty ice at temperatures below 0 °C.</p> Full article ">Figure 5
<p>Types of samples in the spectral library of ices (0, 2, 4 and 6 ppm of very fine, &lt;2 mm, lapilli with andesitic composition). In the case of dirty ices, we do not have “ground truth” to compare or validate the results. The four ice ratios were regionalized to the entire Clementine image and we then visualized the results.</p> Full article ">Figure 6
<p>Correlation between exposure age (38Ar) and 7 Li concentration in lunar samples without 62255 and 74220.</p> Full article ">Figure 7
<p>Relations between altitude (m) of samples and concentration of 7 Li.</p> Full article ">Figure 8
<p>Five spectra types of lunar samples obtained in PCA analysis.</p> Full article ">Figure 9
<p>Detailed cartographic expression of C5 (Orange and Glasses soils) spectral index crossing with the Apollo sample 74220.</p> Full article ">Figure 10
<p>Extended lithium (ppm) and 7 Li‰ estimation models. Detailed mapping in the area of the Apollo 15 landing site. The base maps are Clementine image (500 m/pixel) and LOLA (50 m/pixel). The projection system is GCS_moon_2000.</p> Full article ">Figure 11
<p>Attempt to validate the extended models with the eight lunar samples.</p> Full article ">Figure 12
<p>Correlations between values of relief variables derived from DEM_LOLA and lithium and 7 Li concentrations. The analysis were performed with a simulated population of 296 samples concentrated in the area of Apollo 15 landing site.</p> Full article ">Figure 13
<p>ADS Vis_Nir spectral library for ices.</p> Full article ">Figure 14
<p>Cartographic expression of the spectral ice indices (ICELx1, ICELx2 and ICELx3) across the entire Clementine image. The frequencies of the values obtained by each of the indices in the Clementine image can be consulted in the corresponding histograms.</p> Full article ">Figure 15
<p>Example of regolith with ice probability distribution.</p> Full article ">
18 pages, 17303 KiB  
Article
Constraints on the Fault Dip Angles of Lunar Graben and Their Significance for Lunar Thermal Evolution
by Kai Zhu, Jianzhong Liu, Gregory Michael, Danhong Lei and Xuejin Zeng
Remote Sens. 2024, 16(1), 107; https://doi.org/10.3390/rs16010107 - 26 Dec 2023
Cited by 1 | Viewed by 1292
Abstract
Lunar grabens are the largest tensional linear structures on the Moon. In this paper, 17 grabens were selected to investigate the dips and displacement–length ratios (γ) of graben-bounding faults. Several topographic profiles were generated from selected grabens to measure their rim [...] Read more.
Lunar grabens are the largest tensional linear structures on the Moon. In this paper, 17 grabens were selected to investigate the dips and displacement–length ratios (γ) of graben-bounding faults. Several topographic profiles were generated from selected grabens to measure their rim elevation, width and depth through SLDEM2015 (+LOLA) data. The differences in rim elevation (∆h) and width (∆W) between two topographic profiles on each graben were calculated, yielding 146 sets of data. We plotted ∆h vs. ∆W for each and calculated the dip angle (α) of graben-bounding faults. A dip of 39.9° was obtained using the standard linear regression method. In order to improve accuracy, large error data were removed based on error analysis. The results, 49.4° and 52.5°, were derived by the standard linear regression and average methods, respectively. Based on the depth and length of grabens, the γ value of the graben-bounding normal fault is also studied in this paper. The γ value is 3.6 × 10−3 for lunar normal faults according to the study of grabens and the Rupes Recta normal fault. After obtaining the values of α and γ, the increase in lunar radius indicated by the formation of grabens was estimated. We suggest that the lunar radius has increased by approximately 130 m after the formation of grabens. This study could aid in the understanding of normal fault growth and provide important constraints on the thermal evolution of the Moon. Full article
(This article belongs to the Special Issue Future of Lunar Exploration)
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<p>Lunar Reconnaissance Orbiter (LRO) Wide-Angle Camera (WAC) images of grabens (<b>a</b>,<b>b</b>), rilles (<b>c</b>) and crater-floor fractures (<b>d</b>).</p> Full article ">Figure 2
<p>Distribution of lunar grabens. The base image is WAC mosaic.</p> Full article ">Figure 3
<p>WAC image (<b>a</b>) and block sketch (<b>b</b>,<b>c</b>) of a lunar graben showing dependence of width on elevation; <span class="html-italic">α</span> may be calculated using Equation (1). (<b>b</b>) is modified from [<a href="#B8-remotesensing-16-00107" class="html-bibr">8</a>]. (<b>c</b>) shows the calculation principle of fault dip. AB and CD represent graben rim at the topographic high and low positions, respectively. The line segment EF in (<b>c</b>) indicate width of downthrow block.</p> Full article ">Figure 4
<p>Topographic and slope profiles at the topographic high (<b>a</b>,<b>c</b>) and low (<b>b</b>,<b>d</b>) position. A, B, C, and D in <a href="#remotesensing-16-00107-f004" class="html-fig">Figure 4</a> equal to that in <a href="#remotesensing-16-00107-f003" class="html-fig">Figure 3</a>a.</p> Full article ">Figure 5
<p>Vertical view (<b>a</b>) and sectional drawing (<b>b</b>) of a lunar graben.</p> Full article ">Figure 6
<p>The eight profiles on Repsold Graben (<b>a</b>) and ∆<span class="html-italic">W</span> vs. ∆<span class="html-italic">h</span> plots for all the data (<b>b</b>). N is the number of measurements plotted in this figure.</p> Full article ">Figure 7
<p>Various factors that affect the calculation results. (<b>a</b>) Basalt filling; (<b>b</b>) ejecta filling; (<b>c</b>) collapse; (<b>d</b>) reformed by later tectonic movements.</p> Full article ">Figure 8
<p>Uplift along the Repsold Graben. (<b>a</b>) is the WAC image of Repsold Graben, and (<b>b</b>) shows the topographic feature of Repsold Graben. (<b>c</b>) is the topographic profile in (<b>a</b>,<b>b</b>) (line segment GH).</p> Full article ">Figure 9
<p>Topographic and slope profiles at the edge (profile 1) and middle (profile 2) of the Rupes Recta fault. (<b>a</b>) is the WAC image of the Rupes Recta fault. (<b>b</b>,<b>d</b>) are topographic profiles. (<b>c</b>,<b>e</b>) are slope profiles.</p> Full article ">Figure 10
<p>Statistics of dips that were calculated from ∆<span class="html-italic">h</span> &lt; 200 m data.</p> Full article ">Figure 11
<p>∆<span class="html-italic">W</span> vs. ∆h plots after removing ∆<span class="html-italic">h</span> &lt; 200 m data (<b>a</b>) and negative values (<b>b</b>).</p> Full article ">Figure 12
<p>The <span class="html-italic">D–L</span> plot of grabens. The <span class="html-italic">D</span> values in the (<b>a</b>) are calculated from depths, and the <span class="html-italic">D</span> values in the (<b>b</b>) include the depth reduction caused by degradation. The Graben Hippalus is split into two independent grabens for the purpose of the calculation. Therefore, 18 sets of data were obtained from 17 grabens.</p> Full article ">
16 pages, 8359 KiB  
Article
Inversion of the Lunar Subsurface Rock Abundance Using CE-2 Microwave Brightness Temperature Data
by Wei Yang, Guoping Hu, Fan Yang and Wenchao Zheng
Remote Sens. 2023, 15(20), 4895; https://doi.org/10.3390/rs15204895 - 10 Oct 2023
Viewed by 1523
Abstract
The rock strongly affects the surface and subsurface temperature due to its different thermophysical properties compared to the lunar regolith. The brightness temperature (TB) data observed by Chang’E-1 (CE-1) and Chang’E-2 (CE-2) microwave radiometers (MRM) give us a chance to retrieve the lunar [...] Read more.
The rock strongly affects the surface and subsurface temperature due to its different thermophysical properties compared to the lunar regolith. The brightness temperature (TB) data observed by Chang’E-1 (CE-1) and Chang’E-2 (CE-2) microwave radiometers (MRM) give us a chance to retrieve the lunar subsurface rock abundance (RA). In this paper, a thermal conductivity model with an undetermined parameter β of the mixture has been employed to estimate the physical temperature profile of the mixed layer (rock and regolith). Parameter β and the physical temperature profile of the mixed layer are constrained by the Diviner Channel 7 observations. Then, the subsurface RA on the 16 large (Diameter > 20 km) Copernican-age craters of the Moon is extracted from the average nighttime TB of the CE-2 37 GHz channel based on our previous rocky TB model. Two conclusions can be derived from the results: (1) the subsurface RA values are usually greater than the surface RA values retrieved from Diviner observations of the studied craters; (2) the spatial distribution of subsurface RA extracted from CE-2 MRM data is not necessarily consistent with the surface RA detected by Diviner data. For example, there are similar RA spatial distributions on both the surface and subsurface in Giordano Bruno, Necho, and Aristarchus craters. However, the distribution of subsurface RA is obviously different from that of surface RA for Copernicus, Ohm, Sharonov, and Tycho craters. Full article
(This article belongs to the Special Issue Future of Lunar Exploration)
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<p>The effect of the parameter <math display="inline"><semantics> <mrow> <mi>β</mi> </mrow> </semantics></math> on the surface temperature at the lunar center (0<math display="inline"><semantics> <mrow> <mo>°</mo> </mrow> </semantics></math>N, 0<math display="inline"><semantics> <mrow> <mo>°</mo> </mrow> </semantics></math>E), <math display="inline"><semantics> <mrow> <mi>R</mi> <mi>A</mi> <mo>=</mo> <mn>0.0027</mn> </mrow> </semantics></math> is the average surface RA obtained from Diviner. The top figure shows the diurnal variation in surface temperature with <math display="inline"><semantics> <mrow> <mi>β</mi> </mrow> </semantics></math> and the bottom figure shows the nighttime variation in surface temperature with <math display="inline"><semantics> <mrow> <mi>β</mi> </mrow> </semantics></math>.</p> Full article ">Figure 2
<p>Comparison of the observed surface temperature data and the simulated results at the lunar center region (<b>top</b>) and CE-3 landing site (<b>bottom</b>).</p> Full article ">Figure 3
<p>The effect of the subsurface RA on surface temperature and 37 GHz microwave TB. (<b>a</b>) The effect of the subsurface RA on diurnal (<b>top</b>) and nighttime (<b>bottom</b>) surface temperature. (<b>b</b>) The effect of the subsurface RA on 37 GHz microwave TB.</p> Full article ">Figure 4
<p>Locations of all the studied craters. The background mosaic map is the surface RA retrieved from Diviner. The markers are abbreviations for the selected craters, whose descriptions are shown in <a href="#remotesensing-15-04895-t001" class="html-table">Table 1</a>.</p> Full article ">Figure 5
<p>The distribution of the lunar subsurface rocks. All of the images are the IDW interpolation results produced by ArcMap. The background mosaic images are from the LRO wide-angle camera (WAC).</p> Full article ">Figure 6
<p>The relative distribution of nighttime regolith temperature obtained from Diviner and nighttime microwave TB of CE-2 37 GH. (<b>a</b>) The relative distribution of nighttime regolith temperature required from Diviner at the Copernicus, Ohm, Sharonov, and Tycho craters. (<b>b</b>) The relative distribution of nighttime microwave TB of CE-2 37 GHz at the Copernicus, Ohm, Sharonov, and Tycho craters. The red color represents higher temperature, and the blue color represents lower temperature.</p> Full article ">Figure 7
<p>Effects of H-parameter on nighttime microwave TB (<b>left</b>) and inversion subsurface RA (<b>right</b>).</p> Full article ">Figure 8
<p>Effects of rocks’ loss tangent on nighttime microwave TB (<b>left</b>) and inversion subsurface RA (<b>right</b>).</p> Full article ">
20 pages, 11983 KiB  
Article
Slip Estimation Using Variation Data of Strain of the Chassis of Lunar Rovers Traveling on Loose Soil
by Kojiro Iizuka and Kohei Inaba
Remote Sens. 2023, 15(17), 4270; https://doi.org/10.3390/rs15174270 - 30 Aug 2023
Viewed by 19141
Abstract
The surface of the Moon and planets have been covered with loose soil called regolith, and there is a risk that the rovers may stack, so it is necessary for them to recognize the traveling state such as its posture, slip behavior, and [...] Read more.
The surface of the Moon and planets have been covered with loose soil called regolith, and there is a risk that the rovers may stack, so it is necessary for them to recognize the traveling state such as its posture, slip behavior, and sinkage. There are several methods for recognizing the traveling state such as a system using cameras and Lidar, and they are used in real exploration missions like Mars Exploration Rovers of NASA/JPL. When a rover travels and travels across loose soil with steep slopes like a side wall of a crater on the lunar surface, the rover has side slipping. It means that its behavior makes the rover slip down to the valley direction. Even if this detection uses sensors like a camera and Lidar or other controlling systems like SLAM (Simultaneous Localization and Mapping), it would be too difficult for the rover to avoid slipping down to valley direction, because it is not able to detect the traction or resistance given from ground by individual wheel of the rover, as the traction of individual wheel of the rover is not clear. This means that the movement of the rover appeared by integrating the traction of all wheels mounted on the rover. Even if the localization by sensors is carried out, the location would be the location after slipping down. This is because when traveling on unstable ground, the driving force of each individual wheel cannot be accurately predicted, and the sum of the driving force of all wheels is the motion of the rover, which is detected after the position changes. Therefore, if the rover obtains information on the traction of each wheel, its maneuver to change its posture would work sooner and it would be able to travel more efficiently than in a state without that information. Because the onboard computer of rovers can identify their location and state from the information of the traction of each wheel, they can decide the next work carefully and in detail. From these tasks, we focused on the intrinsic sensation of a biological function like a human body and aimed to develop a system that recognizes the traveling state (slip condition) from the shape deformation of the chassis. In this study, we experimentally verified the relationship between the change in strain, which is the amount of deformation acting on the chassis, and the traveling state while the wheel is traveling. From the experimental results, we confirmed that the strain in the chassis was displaced dynamically and that the strain changed oscillatory while the wheel was traveling. In addition, based on the function of muscle spindles as mechanoreceptors, we discussed two methods of analyzing strain change: nuclear chain fiber analysis and nuclear bag fiber analysis. These analyses mean that the raw data of the strain are updated to detect the characteristic strain elements of a chassis while the wheel is traveling through loose soil. Eventually, the slipping state could be estimated by updating the data of a lot of strain raw data, and it was confirmed that the traveling state could be detected. Full article
(This article belongs to the Special Issue Future of Lunar Exploration)
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<p>Outline of the traveling state (slip condition) detection system.</p> Full article ">Figure 2
<p>Terramechanics model for a single wheel.</p> Full article ">Figure 3
<p>Overview of traversing state. (<b>a</b>) Traversing state on rigid surface. (<b>b</b>) Traversing state on loose soil.</p> Full article ">Figure 4
<p>Deformation of chassis and one traction working.</p> Full article ">Figure 5
<p>Improvement of driving performance of the current system and the proposed system with strain detection sensing. (<b>a</b>) Current rover’s system. (<b>b</b>) Rover system including strain detection sensing (proposed sensing).</p> Full article ">Figure 6
<p>Overview of experimental setting for measurement chassis experiment.</p> Full article ">Figure 7
<p>Actual condition of the experiment.</p> Full article ">Figure 8
<p>State of strain at traveling rigid surface.</p> Full article ">Figure 9
<p>State of strain at traveling loose soil.</p> Full article ">Figure 10
<p>Schematic diagram of a muscle spindle.</p> Full article ">Figure 11
<p>Analysis method. (<b>a</b>) Analysis with “nuclear chain fibers”-like approach. (<b>b</b>) Analysis with “nuclear bag fibers”-like approach.</p> Full article ">Figure 12
<p>Overview of experimental setting for interrelationship verification experiment.</p> Full article ">Figure 13
<p>Vertical force vs. strain.</p> Full article ">Figure 14
<p>Slip ratio vs. strain.</p> Full article ">Figure 15
<p>Comparison with drawbar pull. (<b>a</b>) Slip ratio vs. drawbar pull. (<b>b</b>) Drawbar pull vs. strain.</p> Full article ">Figure 16
<p>Results of frequency analysis for strains (changing vertical force, rigid surface).</p> Full article ">Figure 17
<p>Results of frequency analysis for strains (changing vertical force, loose soil).</p> Full article ">Figure 18
<p>Results of frequency analysis for strains (changing slip ratio, rigid surface).</p> Full article ">Figure 19
<p>System of particle image velocimetry [<a href="#B41-remotesensing-15-04270" class="html-bibr">41</a>].</p> Full article ">Figure 20
<p>Motion of the particles at different slip rates. (<b>a</b>) Visualization of groups of soil grain at low slip conditions. (<b>b</b>) Visualization of groups of soil grain at high slip conditions.</p> Full article ">Figure 20 Cont.
<p>Motion of the particles at different slip rates. (<b>a</b>) Visualization of groups of soil grain at low slip conditions. (<b>b</b>) Visualization of groups of soil grain at high slip conditions.</p> Full article ">
36 pages, 11148 KiB  
Article
A Measurement Method for Cislunar Spacecraft Based on Connected Element Interferometry and BeiDou-3 Interplanetary Link in Future Lunar Exploration
by Zefu Gao, Wenge Yang, Hongbin Ma, Fei Teng, Chao Li, Xuejian Li, Yuxin Wang and Yiwen Jiao
Remote Sens. 2023, 15(15), 3744; https://doi.org/10.3390/rs15153744 - 27 Jul 2023
Cited by 1 | Viewed by 2310
Abstract
To meet the urgent need for high-precision tracking and reliable cataloging of non-cooperative targets in the Earth–Moon space, this paper proposes a GNSS Inter-Satellite Link and Connected Element Interferometry (CEI)-based measurement method for high-value cislunar space targets. Firstly, the general flow and basic [...] Read more.
To meet the urgent need for high-precision tracking and reliable cataloging of non-cooperative targets in the Earth–Moon space, this paper proposes a GNSS Inter-Satellite Link and Connected Element Interferometry (CEI)-based measurement method for high-value cislunar space targets. Firstly, the general flow and basic scenario of the proposed method are given, followed by the mathematical model which, mainly includes four parts: (i) dynamical constraint equations for targets; (ii) GNSS-based interplanetary link for irradiation of targets; (iii) transmission loss equation of GNSS inter-satellite link signal in Earth–Moon space; (iv) CEI-based precision measurements of targets. On this basis, the full process link budget analysis is carried out, followed by the performance evaluation, which includes the reception performance of CEI receiving arrays and the measurement accuracy of targets. The feasibility of the proposed method is evaluated and verified in experiments, and it is illustrated that (i) for inter-satellite link visibility analysis, at least 20 satellites can simultaneously provide inter-satellite link signals to the Earth–Moon space targets, with a single GEO satellite up to 8.5 h continuously, while the chain access can be available at up to 73,000 km, with the angle ranging from −80∘ to 360∘; (ii) the Max Duration of Chain Access for BD3-lunarprobe-CEI (from 24 March 2023 04:00:00.000 to 31 March 2023 10:00:00.000) is 50,998.804 s/day, with a Total Duration of 358,408.797 s in 7 days; (iii) for link budget and measurement accuracy analysis, even beyond the farthest Earth–Moon Lagrangian point, the C/N0 will be above 56.1 dBHZ, while even approaching the distances of 4.5×105km, the σDLL and σFLL will be below 5.345 m and 3.475×10−4 m/s, respectively, and the final measurement error will remain at 62.5 m with the proposed method. The findings of this paper could play a key role in future increasingly serious space missions, such as Earth–Moon space situational awareness, and will have a broad application prospect, if put into actual testing and operations. Full article
(This article belongs to the Special Issue Future of Lunar Exploration)
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<p>Schematic diagram of the relevant range in cislunar space.</p> Full article ">Figure 2
<p>Trajectory of the BeiDou-3 satellites (16 April 2023, 01:00 BDT).</p> Full article ">Figure 3
<p>General flow of the CEI and BDS-3 inter-satellite link-based measurement method for cislunar non-cooperative targets.</p> Full article ">Figure 4
<p>Overview of the non-cooperative target tracking and measurement scenario in cislunar space based on the CEI and BDS-3 inter-satellite link.</p> Full article ">Figure 5
<p>The dynamical constraint of non-cooperative spacecraft in cislunar space.</p> Full article ">Figure 6
<p>Complete link schematic for the measurements of non-cooperative targets.</p> Full article ">Figure 7
<p>The 2D simulation scenario of Beidou-3’s sub-star point trajectory.</p> Full article ">Figure 8
<p>The specific location of the CEI measurement station in the 2D simulation scenario.</p> Full article ">Figure 9
<p>The spatial extent of the Earth–Moon studied in this paper (which highlights the Beidou-3 constellation): (<b>a</b>) in earth inertial axes (<b>b</b>) in moon inertial axes.</p> Full article ">Figure 10
<p>Simulation of lunar exploration mission of the cislunar spacecraft (from launch, maneuver, to propagate): (<b>a</b>) in earth inertial axes (<b>b</b>) in moon inertial axes.</p> Full article ">Figure 11
<p>Key performance indicators of lunarprobe-1 during lunar exploration mission: (<b>a</b>) J2000 Classical Orbit Elements (<b>b</b>) inertial position and velocity (<b>c</b>) LLA position.</p> Full article ">Figure 12
<p>The adjusted trajectory of lunar exploration mission of the cislunar spacecraft with more constraints (in 100 iterations): (<b>a</b>) in earth inertial axes (<b>b</b>) in moon inertial axes.</p> Full article ">Figure 13
<p>Simulation of link budget analysis in cislunar space based on CEI and BeiDou-3 interstellar link: (<b>a</b>) in earth inertial axes (<b>b</b>) in moon inertial axes.</p> Full article ">Figure 14
<p>The access time between lunarprobe-1 and Beidou-3 constellation.</p> Full article ">Figure 15
<p>The chain access AER of the link from Beidou-3 constellation to lunarprobe-1.</p> Full article ">Figure 16
<p>The accessibility and duration time of the link from Beidou-3 constellation to lunarprobe-1.</p> Full article ">Figure 17
<p>The accessibility of the link from Beidou-3 constellation to lunarprobe-1 to CEI.</p> Full article ">Figure 18
<p>The chain access AER of the link from Beidou-3 constellation to lunarprobe-1 to CEI (in Beijing Station).</p> Full article ">Figure 19
<p>The chain access AER of the link from Beidou-3 constellation to lunarprobe-1 to CEI (in Kashi Station).</p> Full article ">Figure 20
<p>Carrier-to-noise ratio at the CEI receiver in the case of spacecraft RCS variations (BD-3 in GEO/IGSO).</p> Full article ">Figure 21
<p>Carrier-to-noise ratio at the CEI receiver in the case of CEI Receiving Antenna Aperture variations (BD-3 in GEO/IGSO).</p> Full article ">Figure 22
<p>Carrier-to-noise ratio at the CEI receiver in the case of CEI Receiving Antenna Efficiency variations (BD-3 in GEO/IGSO).</p> Full article ">Figure 23
<p>Pseudocode ranging performance of CEI receiver arrays in the case of spacecraft RCS variations (BD-3 in GEO/IGSO).</p> Full article ">Figure 24
<p>Pseudocode ranging performance of CEI receiver arrays in the case of CEI Receiving Antenna Aperture variations (BD-3 in GEO/IGSO).</p> Full article ">Figure 25
<p>Pseudocode ranging rate performance of CEI receiver arrays in the case of spacecraft RCS variations (BD-3 in GEO/IGSO).</p> Full article ">Figure 26
<p>Pseudocode ranging rate performance of CEI receiver arrays in the case of CEI Receiving Antenna Aperture variations (BD-3 in GEO/IGSO).</p> Full article ">Figure 27
<p>Final measurement accuracy for cislunar spacecraft through proposed method (BD-3 in GEO/IGSO).</p> Full article ">
17 pages, 8950 KiB  
Article
Improvement of Lunar Surface Dating Accuracy Utilizing Crater Degradation Model: A Case Study of the Chang’e-5 Sampling Area
by Feiyue Zhao, Wei Zuo and Chunlai Li
Remote Sens. 2023, 15(9), 2463; https://doi.org/10.3390/rs15092463 - 8 May 2023
Cited by 1 | Viewed by 1773
Abstract
Taking the Chang’e-5 (CE-5) sampling area as an example, this study carried out an investigation on improving the crater size-frequency distribution (CSFD) dating accuracy of lunar surface geologic units based on the crater degradation model. We constructed a three-parted crater degradation model, which [...] Read more.
Taking the Chang’e-5 (CE-5) sampling area as an example, this study carried out an investigation on improving the crater size-frequency distribution (CSFD) dating accuracy of lunar surface geologic units based on the crater degradation model. We constructed a three-parted crater degradation model, which consists of the diffusion equation describing crater degradation and equations describing the original crater profile for small craters (D < 1 km) and larger craters (D ≥ 1 km). A method that can improve the accuracy of CSFD dating was also proposed in this study, which utilizes the newly constructed degradation model to simulate the degradation process of the craters to help determine the crater degradation process and screen out the craters suitable for CSFD analysis. This method shows a good performance in regional dating. The age determined for the CE-5 sampling area is 2.0 ± 0.2 Ga, very close to the 2.03 ± 0.004 Ga of isotopic dating result of the returned sample. We found that the degradation state of the craters simulated by our constructed degradation model is highly consistent with the real existing state of the craters in terms of their topographic, geomorphological, and compositional (e.g., FeO) features. It fully demonstrates that the degradation model proposed in this study is effective and reliable for describing and distinguishing the degradation state of craters over time due to the cumulative effects of small craters. The proposed method can effectively distinguish between diffusively degraded (which conform to the degradation model) and non-diffusively degraded (which do not conform to the degradation model) craters and improve the CSFD accuracy through the selection of the craters. This not only provides an effective solution to the problem of obtaining a more “exact” frequency distribution of craters, which has long plagued the practical application of the CSFD method in dating the lunar surface but also advances our understanding of the evolutionary history of the geologic units of the study area. The results of this work are important for the in-depth study of the formation and evolution of the moon, especially for lunar chronology. Full article
(This article belongs to the Special Issue Future of Lunar Exploration)
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<p>Flowchart of the method to improve the lunar crater dating accuracy using the crater topography degradation model.</p> Full article ">Figure 2
<p>Image and crater size distribution of the study area. The area (left column) outlined in green was the region upon which craters were identified, excluding the blue regions which were identified as clusters of secondary craters.</p> Full article ">Figure 3
<p>Three results of the crater randomness analysis: (<b>a</b>) non−random, clustered distribution of craters, (<b>b</b>) random distribution of craters, and (<b>c</b>) ordered than random.</p> Full article ">Figure 4
<p>Crater profile simulated using the Crater Degradation Model. (<b>a</b>) Original crater profile ((r<sub>x</sub>,H<sub>x</sub>) are the coordinates of any point located on the crater profile, r<sub>x</sub> is the distance between the point and the central axis of the crater, H<sub>x</sub> is the elevation where the point is located; R<sub>0</sub> is the crater radius, and R<sub>ce</sub> is the crater ejecta blanket radius (for craters with D &lt; 1 km)). (<b>b</b>) Diagram of the crater profile after a certain time degradation: the red line is the original crater profile, the blue line is the degraded crater profile, H<sub>0</sub> is the original crater rim height, and H<sub>D</sub> is the crater rim height after degradation.</p> Full article ">Figure 5
<p>Height of the crater rim after the crater (0 &lt; D &lt; 15 km) experienced different time degradation.</p> Full article ">Figure 6
<p>Diagram of the height extraction of the crater rim of the existing crater. (<b>a</b>) Image of the crater with 6 points Fin1−Fin6 selected at 60° intervals along the crater rim. (<b>b</b>) Elevation profile of the crater after two points Fin1 and Fin4; H<sub>Fin1</sub> and H<sub>Fin4</sub> are the elevation values from the crater rim where Fin1 and Fin4 are located to the lunar surface (LS), respectively.</p> Full article ">Figure 7
<p>Crater size and crater rim height distribution. (<b>a</b>) for craters with 0.5 km ≤ D &lt; 1 km in the study area, (<b>b</b>) for craters with D ≥ 1 km. Black dots are diffusively degraded craters and red dots are non-diffusively degraded craters. The height of the crater rim after 2.0 Ga degradation is used as an example to show the screening of diffusively degraded craters and non-diffusively degraded craters using the crater degradation model.</p> Full article ">Figure 8
<p>Plots of the results of crater randomness analysis in each diameter range in the study area. (<b>a</b>–<b>f</b>) Plots of the analysis results based on the M2CND clustering method; (<b>g</b>,<b>h</b>) plots of the analysis results based on the MCND clustering method; (<b>i</b>) n–sigma plots of the crater randomness analysis in each diameter range.</p> Full article ">Figure 9
<p>Plot of the results of the size—rim height distribution of craters (D ≥ 500 m) in the study area. (<b>a</b>) The distribution of H<sub>D</sub> and H<sub>Fin</sub> of craters (0.5 km ≤ D &lt; 1 km). The blue, red, and green lines indicate the H<sub>D(1.0Ga)</sub>, H<sub>D(2.0Ga)</sub>, and H<sub>D(3.0Ga)</sub> of each crater fitted by the crater topography degradation model, respectively, and the black dots indicate the H<sub>Fin</sub> of each crater in the CE-5 landing area. (<b>b</b>) The distribution of H<sub>D</sub> and H<sub>Fin</sub> of craters (D ≥ 1 km). Black dots mean to meet the diffusive degradation process, and red dots means not to meet the diffusive degradation process.</p> Full article ">Figure 10
<p>AMA of the geologic unit where the study area is located, calculated using the crater screened by the method of this paper. Blue is the age calculated by applying NPF1983, and orange is the age calculated by applying NPF2001. The dotted lines are the 5% and 3% saturation lines; the saturation function formula applied is N<sub>cum</sub> = 1.54 D<sup>−2</sup> [<a href="#B50-remotesensing-15-02463" class="html-bibr">50</a>].</p> Full article ">Figure 11
<p>Plot of the results of estimating the surface age of the study area using different crater data. Blue is the age (2.0 Ga) calculated from 102 diffusively degraded craters screened by the method proposed in this study, purple is the age (3.24 Ga) calculated from all D ≥ 500 m craters, and tan is the age (3.96 Ga) calculated from 3 large non-diffusively degraded craters. The dotted lines are the 5% and 3% saturation lines; the saturation function formula applied is N<sub>cum</sub> = 1.54 D<sup>−2</sup> [<a href="#B50-remotesensing-15-02463" class="html-bibr">50</a>].</p> Full article ">Figure 12
<p>Simulated degradation state of diffusively degraded craters in the study area compared with the existing actual state (D ≥ 1 km). Figures (<b>a1</b>–<b>a7</b>) are the simulated degradation state profiles of craters, the red line is the profile of the crater, and the blue line is the profile after the crater degradation is simulated using the crater degradation model. (<b>b1</b>–<b>b7</b>) are the existing elevation profiles of craters, (<b>c1</b>–<b>c7</b>) are the existing state images of craters, and (<b>d1</b>–<b>d7</b>) are the FeO content distribution maps of crater, the black arrow points to the ejecta of the crater.</p> Full article ">Figure 13
<p>Simulated degradation state of non-diffusively degraded craters in the study area compared with the existing actual state (D ≥ 1 km). Figures (<b>a1</b>–<b>a5</b>) are the simulated degradation state profiles of craters, the red line is the profile of the crater, and the blue line is the profile after the crater degradation is simulated using the crater degradation model. (<b>b1</b>–<b>b5</b>) are the existing elevation profiles of craters, (<b>c1</b>–<b>c5</b>) are the existing state images of craters, and (<b>d1</b>–<b>d5</b>) are the FeO content distribution maps of crater.</p> Full article ">
22 pages, 7862 KiB  
Article
New Lunar Crater Production Function Based on High-Resolution Images
by Jianan Liu, Zongyu Yue, Kaichang Di, Sheng Gou and Yangting Lin
Remote Sens. 2023, 15(9), 2421; https://doi.org/10.3390/rs15092421 - 5 May 2023
Viewed by 2511
Abstract
The lunar crater production function describes the general pattern of the size–frequency distribution of craters on the lunar surface, and it is the foundation of the surface dating method via crater counting. In addition, the lunar crater production function has been extended to [...] Read more.
The lunar crater production function describes the general pattern of the size–frequency distribution of craters on the lunar surface, and it is the foundation of the surface dating method via crater counting. In addition, the lunar crater production function has been extended to other celestial bodies and used to analyze the meteorite flux of the inner solar system. The basic process of establishing the lunar crater production function is to map in an ideal way the primary craters in different geological units, and then to normalize all of the corresponding size–frequency distributions using a mathematical model. Currently, the most widely used lunar crater production functions have been established based on the images acquired in the last century. However, now they can be refined with newly obtained high-resolution images. In this research, we mapped all of the primary craters in 13 regions on the lunar surface with the images acquired using the narrow angle camera and wide angle camera onboard the Lunar Reconnaissance Orbiter, and then we fitted the lunar crater production function with a polynomial. The resultant new lunar crater production function is largely comparable with the previous results, and the difference is mainly at the large diameter end. We analyzed the uncertainty of model fitting as well as the difference in the crater measurements and demonstrated the reliability of the new production function. It is expected to refine the lunar surface dating models, which can provide more accurate information on the impact rate in related studies. Full article
(This article belongs to the Special Issue Future of Lunar Exploration)
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Figure 1

Figure 1
<p>Workflow of this research. All downloaded LROC NAC images were firstly processed in the ISIS program and then imported into ArcGIS to map the counting areas and craters. The CSFD of each counting area was then obtained and finally normalized to a polynomial to obtain the lunar crater PF.</p> Full article ">Figure 2
<p>Locations of all of the counting areas used in this study. The basemap is a WAC image global mosaic. (<b>a</b>) Distribution of nine areas on the lunar nearside. (<b>b</b>) Distribution of three areas on the lunar farside.</p> Full article ">Figure 3
<p>Counting area and mapped craters surrounding A15LS and the corresponding CSFD in comparison with Neukum et al. [<a href="#B17-remotesensing-15-02421" class="html-bibr">17</a>]. (<b>a</b>) Counting areas (encircled by the blue polygons) and mapped craters (shown in red circles). The yellow star indicates the Apollo 15 lander location. Note that the area encircled by the small blue polygon has been removed as it includes many secondary craters. Two pairs of LRO NAC images were used for this region: M104490494 and M1108260533. (<b>b</b>) The CSFD derived in this study (blue) in comparison with that of Neukum et al. [<a href="#B17-remotesensing-15-02421" class="html-bibr">17</a>] (green). The red dashed line represents the diameter range used in the normalization. The lunar equilibrium function [<a href="#B41-remotesensing-15-02421" class="html-bibr">41</a>] is shown by the black dashed line.</p> Full article ">Figure 4
<p>Counting area and mapped craters in the west of Delisle and the corresponding CSFD in comparison with CSFD derived by König [<a href="#B15-remotesensing-15-02421" class="html-bibr">15</a>]. (<b>a</b>) Counting areas (encircled by the blue polygons) and mapped craters (shown in red circles) in NAC image M1203903057L. (<b>b</b>) The CSFD in this study (blue) in comparison with that by König [<a href="#B15-remotesensing-15-02421" class="html-bibr">15</a>] (green). The lunar equilibrium function [<a href="#B41-remotesensing-15-02421" class="html-bibr">41</a>] is shown by the black dashed line.</p> Full article ">Figure 5
<p>Counting area and mapped craters surrounding the Apollo 17 lunar module landing site and the corresponding CSFD in comparison with König [<a href="#B15-remotesensing-15-02421" class="html-bibr">15</a>] and Hiesinger et al. [<a href="#B44-remotesensing-15-02421" class="html-bibr">44</a>]. (<b>a</b>) Counting areas (encircled by the blue polygons) and mapped craters (shown in red circles) in NAC image M192760872L. (<b>b</b>) The CSFD derived in this study (blue) in comparison with that of König [<a href="#B15-remotesensing-15-02421" class="html-bibr">15</a>] (green) and of Hiesinger et al. [<a href="#B44-remotesensing-15-02421" class="html-bibr">44</a>] (purple). The lunar equilibrium function [<a href="#B41-remotesensing-15-02421" class="html-bibr">41</a>] is shown by the black dashed line.</p> Full article ">Figure 6
<p>Counting area and mapped craters in the ejecta of North Ray crater and the corresponding CSFD in comparison with König [<a href="#B15-remotesensing-15-02421" class="html-bibr">15</a>], Moore et al. [<a href="#B47-remotesensing-15-02421" class="html-bibr">47</a>], and Hiesinger et al. [<a href="#B44-remotesensing-15-02421" class="html-bibr">44</a>]. (<b>a</b>) Counting areas (encircled by the blue polygons) and mapped craters (shown in red circles) in NAC image pair M129187331. (<b>b</b>) The CSFD derived in this study (blue) in comparison with that of König [<a href="#B15-remotesensing-15-02421" class="html-bibr">15</a>] (green), Moore et al. [<a href="#B47-remotesensing-15-02421" class="html-bibr">47</a>] (cyan), and Hiesinger et al. [<a href="#B44-remotesensing-15-02421" class="html-bibr">44</a>] (purple). The lunar equilibrium function [<a href="#B41-remotesensing-15-02421" class="html-bibr">41</a>] is shown by the black dashed line.</p> Full article ">Figure 7
<p>Counting area and mapped craters in the ejecta of Cone crater and the corresponding CSFD in comparison with that of Moore et al. [<a href="#B47-remotesensing-15-02421" class="html-bibr">47</a>]. (<b>a</b>) Counting areas (encircled by the blue polygons) and mapped craters (shown in red circles) in NAC image M114071006L. (<b>b</b>) The CSFD in this study (blue) in comparison with that by Moore et al. [<a href="#B47-remotesensing-15-02421" class="html-bibr">47</a>] (green). The lunar equilibrium function [<a href="#B41-remotesensing-15-02421" class="html-bibr">41</a>] is shown by the black dashed line.</p> Full article ">Figure 8
<p>Counting area and mapped craters in the ejecta of Aristarchus crater and the corresponding CSFD in comparison with König [<a href="#B15-remotesensing-15-02421" class="html-bibr">15</a>] and Zanetti et al. [<a href="#B51-remotesensing-15-02421" class="html-bibr">51</a>]. (<b>a</b>) Counting areas (encircled by the blue polygons) and mapped craters (shown in red circles) in NAC image pair M102472092. (<b>b</b>) The CSFD in this study (blue) in comparison with that by König [<a href="#B15-remotesensing-15-02421" class="html-bibr">15</a>] (green) and Zanetti et al. [<a href="#B51-remotesensing-15-02421" class="html-bibr">51</a>] (purple). The lunar equilibrium function [<a href="#B41-remotesensing-15-02421" class="html-bibr">41</a>] is shown by the black dashed line.</p> Full article ">Figure 9
<p>Counting areas (encircled by the blue polygons) and mapped craters (shown in red circles) in a WAC image global mosaic [<a href="#B23-remotesensing-15-02421" class="html-bibr">23</a>]. (<b>a</b>) The interior of Mare Serenitatis. (<b>b</b>) The area in the southeast of the Mare Crisium. The yellow star indicates the Luna 24 lander location. (<b>c</b>) The bottom of the Mendeleev crater. (<b>d</b>) The area in the Orientale basin between the Cordillera ring and the Rook ring.</p> Full article ">Figure 9 Cont.
<p>Counting areas (encircled by the blue polygons) and mapped craters (shown in red circles) in a WAC image global mosaic [<a href="#B23-remotesensing-15-02421" class="html-bibr">23</a>]. (<b>a</b>) The interior of Mare Serenitatis. (<b>b</b>) The area in the southeast of the Mare Crisium. The yellow star indicates the Luna 24 lander location. (<b>c</b>) The bottom of the Mendeleev crater. (<b>d</b>) The area in the Orientale basin between the Cordillera ring and the Rook ring.</p> Full article ">Figure 10
<p>Counting area (blue circle) and counted craters (red circles) on the ejecta of the Orientale basin defined by Yue et al. [<a href="#B54-remotesensing-15-02421" class="html-bibr">54</a>]. Only craters with <span class="html-italic">D</span> &gt; 10 km above the ejecta blanket were mapped.</p> Full article ">Figure 11
<p>Counting area (blue circle) and counted craters (red circles) within the SPA basin (only the craters with <span class="html-italic">D</span> ≥ 10 km are shown in the figure). The regions marked by cyan are the basalt units located in the SPA basin and they were excluded from the counting area.</p> Full article ">Figure 12
<p>All of the CSFDs and the normalization in this research. (<b>a</b>) The derived CSFDs in the different areas in this research. (<b>b</b>) The normalization result and the fitted lunar crater PF, in which the CSFD from the CE-5 landing area was used as the reference.</p> Full article ">Figure 13
<p>The new PF derived in this research compared with that of Neukum [<a href="#B1-remotesensing-15-02421" class="html-bibr">1</a>] and Neukum et al. [<a href="#B57-remotesensing-15-02421" class="html-bibr">57</a>].</p> Full article ">Figure 14
<p>The CSFDs and the normalized version of three areas in this study. (<b>a</b>) The CSFDs of Orientale—inner (blue), Orientale—ejecta (green), and SPA basin (purple). (<b>b</b>) The CSFD normalized to CE-5.</p> Full article ">Figure 15
<p>Cumulative fit by new PF (<b>left column</b>) and cumulative fit by NPF2001 (<b>right column</b>). (<b>a</b>,<b>b</b>) Crater CSFD of pre-Nectarian terrains; data can be found in Marchi et al. [<a href="#B65-remotesensing-15-02421" class="html-bibr">65</a>]; (<b>c</b>,<b>d</b>) crater CSFD of Nectarian basin; data can be found in Marchi et al. [<a href="#B65-remotesensing-15-02421" class="html-bibr">65</a>]; and (<b>e</b>,<b>f</b>) crater CSFD of terrestrial craters; data can be found in Earth Impact Database [<a href="#B66-remotesensing-15-02421" class="html-bibr">66</a>].</p> Full article ">
13 pages, 10123 KiB  
Communication
Hybrid Volcanic Episodes within the Orientale Basin, Moon
by Shreekumari Mukeshbhai Patel, Harish, Deep Patel, Paras M. Solanki and Mohamed Ramy El-Maarry
Remote Sens. 2023, 15(7), 1801; https://doi.org/10.3390/rs15071801 - 28 Mar 2023
Cited by 1 | Viewed by 2186
Abstract
Basalts from Mare Orientale are representative of lunar flood volcanism, which sheds light on the lunar farside’s thermal and volcanic past. We use Chandrayaan’s Moon Mineralogy Mapper data to examine the spectral and chemical makeup of the volcanic units located in the Orientale [...] Read more.
Basalts from Mare Orientale are representative of lunar flood volcanism, which sheds light on the lunar farside’s thermal and volcanic past. We use Chandrayaan’s Moon Mineralogy Mapper data to examine the spectral and chemical makeup of the volcanic units located in the Orientale basin; the analysis specifically focuses on three formations: Mare Orientale, Lacus Veris, and Lacus Autumni. The main assemblage in these basaltic units consists of calcic augite and ferroaugite. Pyroxenes in the Orientale volcanic units have an average chemical composition of En35.53 Fs34.11 Wo30.35. The trend in the composition of pigeonites and augites suggests that the magma was fractionated as it crystallized. The pyroxene quadrilateral plot’s distinct chemical trends indicate that the Orientale Basin underwent a number of volcanic eruptions from heterogeneous magma sources during the Imbrium to Eratosthenian period. Full article
(This article belongs to the Special Issue Future of Lunar Exploration)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>The inset shows a Lunar Reconnaissance Orbiter Camera (LROC)-Wide Angle Camera (WAC) map with the Mare Orientale of the Moon highlighted by the black box. Based on the research of Whitten et al. (2011), the Orientale basin is shown with the main rings and labelled deposits in the SLDEM data (~59 m/pixel).</p> Full article ">Figure 2
<p>The LROC–WAC map displays the Mare Orientale of the Moon. Spectral maps of the Orientale basin: (<b>a</b>) BD 1900; (<b>b</b>) BD 2300; (<b>c</b>) M<sup>3</sup> FCC image (R: G: B-BD 1900: BD 2300: BD 1250); (<b>d</b>) M<sup>3</sup> 1.578 µm albedo map displaying the differences in albedo between the mare units. Reflectance spectra of different minerals from the basin region are shown in (<b>e</b>) normal and (<b>f</b>) continuum-removed spectra. The red circles in the image denote the sites from which spectral data of different minerals were sampled, as labelled in (<b>c</b>). The solid white lines define the limits of the different mare units. Projection: Stereographic centered at the Orientale basin.</p> Full article ">Figure 3
<p>(<b>a</b>) FeO content retrieval result from M<sup>3</sup> data, (<b>b</b>) TiO<sub>2</sub> content retrieval result from M<sup>3</sup> data, (<b>c</b>) MNF–FCC image (R: G: B-Component 7: 9: 8), and (<b>d</b>) M<sup>3</sup> color composite image (R: G: B-IBD 1000: IBD 2000: M<sup>3</sup> 1.578 µm) demonstrating the differences in the mineral composition of basaltic units. The black circles in the image point to the sites from which the reflectance spectra were sampled. Image projection: Stereographic centered at the Orientale basin.</p> Full article ">Figure 4
<p>(<b>a</b>) BAR vs BC I graph [<a href="#B44-remotesensing-15-01801" class="html-bibr">44</a>]. The area of the OC polygon corresponds to mafic silicate composition of ordinary chondrites, whereas the BA rectangular region represents pyroxene-dominated basaltic achrondrites [<a href="#B44-remotesensing-15-01801" class="html-bibr">44</a>]. (<b>b</b>) BC II vs BC I (corrected) graph of pyroxene spectra from the mare units. (<b>c</b>) Pyroxene quadrilateral (Wo–En–Fs ternary) graph projecting the mole fraction of Ca, Mg, and Fe content in pyroxenes from each mare unit, along with the pyroxene isotherm determined by the graphical thermometer generated by [<a href="#B25-remotesensing-15-01801" class="html-bibr">25</a>]. (<b>d</b>) Comparison between the pyroxene chemistry of the mare units and that of the Apollo, Luna, Chang ‘E-5 (CE-5), and other lunar meteorite samples [<a href="#B41-remotesensing-15-01801" class="html-bibr">41</a>,<a href="#B42-remotesensing-15-01801" class="html-bibr">42</a>,<a href="#B45-remotesensing-15-01801" class="html-bibr">45</a>,<a href="#B46-remotesensing-15-01801" class="html-bibr">46</a>].</p> Full article ">Figure 4 Cont.
<p>(<b>a</b>) BAR vs BC I graph [<a href="#B44-remotesensing-15-01801" class="html-bibr">44</a>]. The area of the OC polygon corresponds to mafic silicate composition of ordinary chondrites, whereas the BA rectangular region represents pyroxene-dominated basaltic achrondrites [<a href="#B44-remotesensing-15-01801" class="html-bibr">44</a>]. (<b>b</b>) BC II vs BC I (corrected) graph of pyroxene spectra from the mare units. (<b>c</b>) Pyroxene quadrilateral (Wo–En–Fs ternary) graph projecting the mole fraction of Ca, Mg, and Fe content in pyroxenes from each mare unit, along with the pyroxene isotherm determined by the graphical thermometer generated by [<a href="#B25-remotesensing-15-01801" class="html-bibr">25</a>]. (<b>d</b>) Comparison between the pyroxene chemistry of the mare units and that of the Apollo, Luna, Chang ‘E-5 (CE-5), and other lunar meteorite samples [<a href="#B41-remotesensing-15-01801" class="html-bibr">41</a>,<a href="#B42-remotesensing-15-01801" class="html-bibr">42</a>,<a href="#B45-remotesensing-15-01801" class="html-bibr">45</a>,<a href="#B46-remotesensing-15-01801" class="html-bibr">46</a>].</p> Full article ">

Review

Jump to: Research

24 pages, 24292 KiB  
Review
In-Situ Radar Observation of Shallow Lunar Regolith at the Chang’E-5 Landing Site: Research Progress and Perspectives
by Feiyang Fang, Chunyu Ding, Jianqing Feng, Yan Su, Ravi Sharma and Iraklis Giannakis
Remote Sens. 2023, 15(21), 5173; https://doi.org/10.3390/rs15215173 - 30 Oct 2023
Cited by 4 | Viewed by 2442
Abstract
China accomplished a historic milestone in 2020 when the mission Chang’e-5 (CE-5) to the Lunar’s surface was successfully launched. An extraordinary component of this mission is the “Lunar Regolith Penetrating Radar” (LRPR) housed within its lander, which currently stands as the most advanced [...] Read more.
China accomplished a historic milestone in 2020 when the mission Chang’e-5 (CE-5) to the Lunar’s surface was successfully launched. An extraordinary component of this mission is the “Lunar Regolith Penetrating Radar” (LRPR) housed within its lander, which currently stands as the most advanced payload in terms of vertical resolution among all penetrating radars employed in lunar exploration. This provides an unprecedented opportunity for high-precision research into the interior structure of the shallow lunar regolith. Previous studies have achieved fruitful research results based on the data from LRPR, updating our perception of the shallow-level regolith of the Moon. This paper provides an overview of the new advancements achieved by the LRPR in observing the basic structure of the shallow regolith of the Moon. It places special emphasis on the role played by the LRPR in revealing details about the shallow lunar regolith’s structure, its estimated dielectric properties, the provenance of the regolith materials from the landing area, and its interpretation of the geological stratification at the landing site. Lastly, it envisions the application and developmental trends of in situ radar technology in future lunar exploration. Full article
(This article belongs to the Special Issue Future of Lunar Exploration)
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Figure 1

Figure 1
<p>The morphological context of the CE-5 landing site (adopted from Li et al. [<a href="#B13-remotesensing-15-05173" class="html-bibr">13</a>]), and CLEP stands for China’s Lunar Exploration Project. (<b>a</b>) The comprehensive geological information was obtained through the Digital Orthophoto Map (DOM) from CE-2, and the position of the CE-5 lander is indicated by a red cross. (<b>b</b>) The Landing camera of CE-5 provided the image of the landing location, and the CE-5 lander is inside the white box. (<b>c</b>) Topographic map captured by panoramic camera of the CE-5.</p> Full article ">Figure 2
<p>FeO (<b>a</b>) and TiO<math display="inline"><semantics> <msub> <mrow/> <mn>2</mn> </msub> </semantics></math> (<b>b</b>) concentration distribution maps in the CE-5 landing location, adopted from Qiao et al. [<a href="#B69-remotesensing-15-05173" class="html-bibr">69</a>].</p> Full article ">Figure 3
<p>Layout diagram of LRPR radar antenna array, adopted form Ding et al. [<a href="#B11-remotesensing-15-05173" class="html-bibr">11</a>]. The inset image in the lower left corner represents the antennas’ position, with the typical spacing between antennas being 12 cm.</p> Full article ">Figure 4
<p>Experimental site and conditions for ground verification of CE-5 LRPR (modified from Xiao et al. [<a href="#B34-remotesensing-15-05173" class="html-bibr">34</a>], Li et al. [<a href="#B35-remotesensing-15-05173" class="html-bibr">35</a>]). (<b>a</b>) The LRPR carried by the lander model. (<b>b</b>) The structure of the test site. (<b>c</b>) Targets excavation. (<b>d</b>) The LRPR deployed on the lunar regolith simulant. (<b>e</b>) The location of the buried targets within the lunar regolith simulant.</p> Full article ">Figure 5
<p>Radar images acquired from the ground experiments, adopted from Feng et al. [<a href="#B79-remotesensing-15-05173" class="html-bibr">79</a>]. (<b>a</b>) Raw radargram. (<b>b</b>) Processed radargram after applying delay correction, bandpass filtering, and sampling calibration. (<b>c</b>) Radargram after background removal.</p> Full article ">Figure 6
<p>Estimation of velocity spectrum for LRPR data (adopted from Feng et al. [<a href="#B79-remotesensing-15-05173" class="html-bibr">79</a>]). (<b>a</b>) Envelope velocity spectrum. (<b>b</b>) The geometric path of electromagnetic wave propagation in two media.</p> Full article ">Figure 7
<p>Core sampling scene of the CE-5 mission on the Moon’s surface (adopted from Zheng et al. [<a href="#B17-remotesensing-15-05173" class="html-bibr">17</a>]. (<b>a</b>) Layout of drilling equipment. (<b>b</b>) Schematic diagram of drilling mechanism. (<b>c</b>) Drilling Scenario.</p> Full article ">Figure 8
<p>Radar imaging results of CE-5 ground experiment [<a href="#B79-remotesensing-15-05173" class="html-bibr">79</a>], in which the arrows indicate areas with clutters. (<b>a</b>–<b>e</b>) are migration results for five different test scenarios proposed in Feng et al. [<a href="#B79-remotesensing-15-05173" class="html-bibr">79</a>]’s paper.</p> Full article ">Figure 9
<p>The CE-5 radar observations on the lunar surface (adopted from Su et al. [<a href="#B23-remotesensing-15-05173" class="html-bibr">23</a>] and Feng et al. [<a href="#B25-remotesensing-15-05173" class="html-bibr">25</a>]). (<b>a</b>) Drill a bit of pressure with depth. (<b>b</b>) Radar imaging before drilling. (<b>c</b>) Radar imaging after drilling. The white line represents the drill pipe. (<b>d</b>–<b>f</b>) are the imaging results of Feng et al. [<a href="#B25-remotesensing-15-05173" class="html-bibr">25</a>].</p> Full article ">Figure 10
<p>Dielectric permittivity and loss tangent estimation using the radar data [<a href="#B23-remotesensing-15-05173" class="html-bibr">23</a>]. (<b>a</b>) a graph showing the estimated permittivity’s histogram. (<b>b</b>) The histogram of the estimated permittivity. (<b>c</b>–<b>f</b>) Comparison of depth and average power, the red fitting curve indicates the attenuation rate of radar waves.</p> Full article ">Figure 11
<p>The findings were obtained through laboratory tests of lunar regolith samples collected during the CE-5 mission for loss tangent and dielectric permittivity (adopted from Su et al. [<a href="#B23-remotesensing-15-05173" class="html-bibr">23</a>]). (<b>a</b>,<b>b</b>) In the frequency band range of 1–3 GHz, the relative loss tangent and dielectric permittivity of the regolith sample from the CE-5 mission were subsequently calculated. (<b>c</b>) Measurements of the Apollo sample were used to determine the link between dielectric permittivity and bulk density. (<b>d</b>) The relationship between the loss tangent and the wt% content of TiO<math display="inline"><semantics> <msub> <mrow/> <mn>2</mn> </msub> </semantics></math> + FeO.</p> Full article ">Figure 12
<p>The craters around the area of CE-5 landing (adopted from Jia et al. [<a href="#B93-remotesensing-15-05173" class="html-bibr">93</a>]). (<b>a</b>) Red circles indicate the locations of each source crater in unit P58. (<b>b</b>) Source craters within 40 km. (<b>c</b>) Source craters within 1 km. Red star indicates the location of the CE-5 lander.</p> Full article ">Figure 13
<p>Geological profile of shallow lunar regolith at the CE-5 landing site (modified from Su et al. [<a href="#B23-remotesensing-15-05173" class="html-bibr">23</a>] and Qian et al. [<a href="#B94-remotesensing-15-05173" class="html-bibr">94</a>]). (<b>a</b>) Geological background images around the landing site [<a href="#B23-remotesensing-15-05173" class="html-bibr">23</a>]; (<b>b</b>) Schematic diagram of the geological profile of shallow lunar regolith revealed by CE-5 radar at a depth of 2.5 m [<a href="#B23-remotesensing-15-05173" class="html-bibr">23</a>]. (<b>c</b>) Stratigraphic structure of the CE-5 landing site [<a href="#B94-remotesensing-15-05173" class="html-bibr">94</a>].</p> Full article ">
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