Special Issue "Lunar Remote Sensing and Applications"

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: 31 August 2020.

Special Issue Editors

Prof. Dr. Shengbo Chen
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Guest Editor
College of Geo-exploration Science and Technology, Jilin University, No. 938 XiMinZhu Street, Chaoyang Distract, Changchun 130026, China
Interests: Remote sensing; Lunar and planetary geology; Geological remote sensing
Special Issues and Collections in MDPI journals
Prof. Dr. Lin Li
E-Mail Website
Guest Editor
Department of Earth Science, Indiana University - Purdue University Indianapolis, 723 West Michigan Street, Indianapolis, IN 46202, USA
Interests: Remote sensing; Lunar and Planetary Geology; Environmental Science; Data processing
Special Issues and Collections in MDPI journals
Prof. Dr. Yuanzhi Zhang
E-Mail
Guest Editor
University of Chinese Academy of Sciences, Chinese Academy of Sciences, No. 20 Datun Road, Chaoyang Distract, Beijing 100101, China
Interests: Remote sensing; Lunar and planetary science; Environmental remote sensing; Image processing

Special Issue Information

Dear Colleagues,

The exploration of the Moon has generated a large volume of various datasets for addressing scientifically important questions on lunar geology, including the origin of the Moon, the origin and evolution of the lunar crust and mantle, the compositional structure of the lunar interior, lunar volcanism and impact cratering processes, regolith evolution and mixing dynamics, space weathering, as well as searching for and utilizing resources for a human future presence on the Moon. Analysis of the rock samples returned by the Apollo and Luna missions has resulted in numerous important discoveries and observations revolving around these scientific questions. However, the lunar samples returned to Earth so far have very limited spatial coverage, and extrapolation of the sample-based geological context to the global or regional scale surface setting of the Moon heavily relies on remote sensing datasets acquired by lunar spacecraft.

Lunar remote sensing images are mainly composed of multi- and hyper-spectral datasets in the visible (VIS), near-infrared (NIR), and shortwave infrared (SWIR), which are sensitive to the mineralogical composition of the lunar surface because of the spectrally diagnostic absorption features of major minerals (e.g. olivine, clinopyroxene, orthopyroxene, ilmenite, plagioclase) and different glasses on the Moon. On the other hand, thermal infrared (TIR) and passive microwave data are definitely necessary for mapping substrate physical properties (temperature, regolith size, thickness and layering) and chemical compositions, which are helpful for refining the classification of the substrate regolith and mare basaltic units and for mapping lunar faults and tectonic units.

Over the past decades, a wealth of remotely-sensed photographic and spectroscopic data have been collected by various lunar missions such as Clementine, Small Missions for Advanced Research in Technology-1 (SMART-1), Lunar Reconnaissance Orbiter, SELENE, Chang’E I-III, and Chandrayaan-1. Additionally, a large fleet of new lunar missions will be launched in next few years by different countries and private sectors. These previously and newly acquired remote sensing data provide unprecedented opportunities to study the Moon by the examination of new ideas and testing data analysis algorithms.

This Special Issue invites manuscripts resulting from the analysis of remote sensing datasets acquired by the latest lunar missions, as well as from lab-measured spectral data with the aim of highlighting the importance of lab spectroscopic and imaging remote sensing in studies of the Moon. The Special Issue also welcomes to manuscripts reporting research results from various observations and measurements by use of photography, X-ray, gamma-ray, gravitational, magnetic, and topographic data, which advance our current knowledge of the Moon. The topics include, but are not limited to the following:

  • Optical remote sensing and data analysis techniques for the identification and mapping of lunar regolith, mineralogy, and lithology;
  • Thermal remote sensing of physical and compositional properties of the lunar surface;
  • Microwave remote sensing of lunar subsurface structure;
  • Radiative transfer models for lunar remote sensing;
  • Integration of remote sensing data with laboratory spectral and compositional measurements;
  • Photogeological analysis of lunar terrains;
  • Photogeological analysis of lunar faults and tectonic units;
  • Photogeological analysis of lunar volcanism;
  • Photogeological analysis of impact craters, South Pole-Aitken (SPA) and other basins;
  • Remote sensing of lunar polar regions and space weathering

Prof. Dr. Shengbo Chen
Prof. Dr. Lin Li
Prof. Dr. Yuanzhi Zhang
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 papers will be 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 2000 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

  • The Moon
  • Remote Sensing
  • Space weathering
  • Regolith, mineral, and rock
  • Lunar crust and interior
  • Lunar volcanism and thermal history
  • Lunar faults and tectonic features
  • Impact craters and ejecta deposits
  • South Pole-Aitken (SPA) and other basins
  • Permanent shadow and ice

Published Papers (3 papers)

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Open AccessArticle
Simulation Study of Moon-Based InSAR Observation for Solid Earth Tides
Remote Sens. 2020, 12(1), 123; https://doi.org/10.3390/rs12010123 - 01 Jan 2020
Abstract
The observation of solid earth tides (SET) provides an important basis for understanding the structure of the earth’s interior, and has long been the focus of research in geoscience. However, actually, there still exist some limitations in capturing its global-scale information only with [...] Read more.
The observation of solid earth tides (SET) provides an important basis for understanding the structure of the earth’s interior, and has long been the focus of research in geoscience. However, actually, there still exist some limitations in capturing its global-scale information only with ground stations. Remote sensing technology can realize large-scale deformation monitoring of high point density constantly. However, it is still difficult for the artificial satellite system to meet the requirements of SET monitoring in terms of field of view and temporal resolution now. In this work, the moon is hypothesized as a new platform for SET observation combined with interferometric synthetic aperture radar (InSAR) technology. Based on the tidal model and lunar ephemeris, the spatial and temporal characteristics of the SET from the lunar view were analyzed. Furthermore, the calculations demonstrate that more abundant SET information can be observed in this view. After comparing various observation modes, the single-station with repeat-pass differential InSAR was selected for this simulation. We mainly considered the restriction of observation geometry on moon-based InSAR under three signal bandwidths, thereby providing a reference for the sensor design. The results demonstrate that the moon-based platform offers the potential to become an optimal SET observation method. Full article
(This article belongs to the Special Issue Lunar Remote Sensing and Applications)
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Open AccessArticle
A Machine Learning Approach to Crater Classification from Topographic Data
Remote Sens. 2019, 11(21), 2594; https://doi.org/10.3390/rs11212594 - 05 Nov 2019
Abstract
Craters contain important information on geological history and have been widely used for dating absolute age and reconstructing impact history. The impact process results in a lot of ejected fragments and these fragments may form secondary craters. Studies on distinguishing primary craters from [...] Read more.
Craters contain important information on geological history and have been widely used for dating absolute age and reconstructing impact history. The impact process results in a lot of ejected fragments and these fragments may form secondary craters. Studies on distinguishing primary craters from secondary craters are helpful in improving the accuracy of crater dating. However, previous studies about distinguishing primary craters from secondary craters were either conducted by manual identification or used approaches mainly concerning crater spatial distribution, which are time-consuming or have low accuracy. This paper presents a machine learning approach to distinguish primary craters from secondary craters. First, samples used for training and testing were identified and unified. The whole dataset contained 1032 primary craters and 4041 secondary craters. Then, considering the differences between primary and secondary craters, features mainly related to crater shape, depth, and density were calculated. Finally, a random forest classifier was trained and tested. This approach showed a favorable performance. The accuracy and F1-score for fivefold cross-validation were 0.939 and 0.839, respectively. The proposed machine learning approach enables an automated method of distinguishing primary craters from secondary craters, which results in better performance. Full article
(This article belongs to the Special Issue Lunar Remote Sensing and Applications)
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Open AccessTechnical Note
Analyzing the Magnesium (Mg) Number of Olivine on the Lunar Surface and Its Geological Significance
Remote Sens. 2019, 11(13), 1544; https://doi.org/10.3390/rs11131544 - 28 Jun 2019
Abstract
Olivine formation is directly related to Mg/Fe content. It is also significant in estimating the geological evolution of the moon. In this study, an estimation model of relative Mg number (Fo#) for lunar olivine was presented through multiple linear regression statistics. Sinus Iridum, [...] Read more.
Olivine formation is directly related to Mg/Fe content. It is also significant in estimating the geological evolution of the moon. In this study, an estimation model of relative Mg number (Fo#) for lunar olivine was presented through multiple linear regression statistics. Sinus Iridum, the Copernicus Crater, and the pyroclastic deposit in the volcanic vents in the southeast of Orientale Basin were selected as the study areas. Olivine distribution was surveyed, and the relative Fo# calculation of olivine was implemented based on Moon Mineralogy Mapper (M3) data. Results demonstrated that olivine in the crater wall of Sinus Iridum and the Copernicus Crater had relatively high Fo#, which reflected the primitive melt. However, the difference in olivine spectral features between Sinus Iridum and the Copernicus Crater indicated different crystallization modes. The olivine in the pyroclastic deposit in the volcanic vents in the southwest of Orientale Basin also presented high Fo#, which indicated that the olivine was formed via rapid cooling crystallization and was accompanied by volcanic glass substances. As a result, the olivine relative Fo# calculated from the estimation model exhibited an important constraint implication for explanation of its causes. Full article
(This article belongs to the Special Issue Lunar Remote Sensing and Applications)
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