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319 results for “Lunar”
Supplementary Data for Wueller et al. (2024): Geologic History of the Amundsen Crater Region Near the Lunar South Pole: Basis for Future Exploration
<p>Supplementary Data for Wueller et al. (2024): Geologic History of the Amundsen Crater Region Near the Lunar South Pole: Basis for Future Exploration</p> <p>Data contains the georeferenced map plate of our geologic map that can be used in any geoinformation system (GIS).</p> <p><strong>If you use these data, please cite BOTH the Planetary Science Journal publication and the Zenodo dataset.</strong></p> <p>Wueller, L., Iqbal, W., Frueh, T., van der Bogert, C. H., & Hiesinger, H. (2024). Geologic history of the Amundsen crater region near the Lunar South Pole: Basis for future exploration. <em>The Planetary Science Journal</em>, <em>5</em>(6), 147. <a href="https://iopscience.iop.org/article/10.3847/PSJ/ad2c04">https://iopscience.iop.org/article/10.3847/PSJ/ad2c04</a></p> <p>Wueller, L., Iqbal, W., Frueh, T., van der Bogert, C. H., & Hiesinger, H. (2024). Supplementary Data for Wueller et al. (2024): Geologic history of the Amundsen crater region near the Lunar South Pole: Basis for future exploration. <em>Zenodo Dataset</em>. <a href="https://doi.org/10.5281/zenodo.10693820" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10693820</a></p> <p>-----------------------------------------------------------------------------------------------------------------------------------------</p> <p>Mapping Scale is 1:100,000</p> <p>Print Scale is 1:1,000,000</p> <p>-----------------------------------------------------------------------------------------------------------------------------------------</p> <p>For further questions contact lwueller@uni-muenster.de</p> <p>Lukas Wueller, Institut für Planetologie, Universität Münster, Germany, June 2024</p>
Lunar Missions Database (MoonDB)
<p>The MoonDB is a database of past and present spacecraft in cislunar space, compiled through publicly available sources. It was designed to better understand the rationale behind missions, focusing on their final results, current status, funding agencies and nations. Particular attention has also been given to the surface of the Moon, where landing sites have been identified. The database is mainly based on data collected by NASA in the Master Catalogue of the NASA Space Science Data Coordinated Archive (NSSDCA) [1]. Other sources are also considered, such as the Satellite Catalog (SATCAT) from the Space-Track project [2], created by the US Combined Force Space Component Command (CFSCC). The work by McDowell (2020) [3] has also been considered an inspiration for this work, although the primary source of information has remained the NASA catalogue.</p> <p>Some structural and logical changes have been introduced to follow the needs of this research project. Following a list provided by the NSSDCA, a certain number of tentative USSR missions were added to the statistics [4]. Most spacecraft were destroyed due to a launch failure and were not disclosed to the public: the available information results from an investigation.</p> <p>Additional information and a version changelog are provided in the readme file.<br> </p>
Datasets for "Mapping Lunar Swirls with Machine Learning: The Application of Unsupervised and Supervised Classification Algorithms in Reiner Gamma and Mare Ingenii"
<p>Final surface reflectance data at 2.6 m/pixel resolution with floating point values are available as GeoTiff and ASCII text files. Definition files for the K-Means and MLC algorithms in classifying swirl units are also available as ASCII text files. See README file for further details.</p> <p>Data used in the research article:</p> <p>Chuang, F.C., M.D. Richardson, J.R. Weirich, A.A. Sickafoose, and D.L. Domingue, 2022. Mapping Lunar Swirls with Machine Learning: The Application of Unsupervised and Supervised Image Classification Algorithms in Reiner Gamma and Mare Ingenii. The Planetary Science Journal, 3:231. doi://10.3847/PSJ/ac8f43</p> <p> </p>
Complementary data for Iqbal et al. (2023): Geological Mapping and Chronology of Lunar Landing Sites: Apollo 14
<p>Complementary data for Iqbal et al. (2023): Geological Mapping and Chronology of Lunar Landing Sites: Apollo 14</p> <p>Data contains the Geotiff of our geologic map that can be used in any geoinformation system (GIS), and map plate for interpreting the map.</p> <p><strong>If you use these data, please cite BOTH the Icarus publication and the Zenodo dataset</strong></p> <p>Iqbal, W., Hiesinger, H., Borisov, D., van der Bogert, C. H., & Head III, J. W. (2023). Geological mapping and chronology of lunar landing sites: Apollo 14. <em>Icarus</em>, <em>406</em>, 115732. <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.icarus.2023.115732" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.icarus.2023.115732</a></p> <p>Iqbal, W., Hiesinger, H., Borisov, D., van der Bogert, C. H., & Head III, J. W. (2023). Complementary data for Iqbal et al. (2023): Geological Mapping and Chronology of Lunar Landing Sites: Apollo 14 [Data set]. In Icarus (Bd. 406, S. 115732). Zenodo. <a href="https://doi.org/10.5281/zenodo.8124259" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.8124259</a></p> <p>--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>For further questions contact <a href="mailto:lwueller@uni-muenster.de" rel="noopener noreferrer nofollow">iqbalw@uni-muenster.de</a></p> <p>Wajiha Iqbal, Institut für Planetologie, Universität Münster, Germany.</p>
Supporting Data Sets for "New Constraints on the Lunar Optical Space Weathering Rate"
<p>Data Sets supporting "New Constraints on the Lunar Optical Space Weathering Rate" submitted to Geophysical Research Letter on 12/18/2020. See Supporting Information (link TBD).</p>
Microwave brightness temperature of lunar south polar region
<p>These are Brightness temperature data of lunar south polar region (≤-80°) obtained by CE-2 MRM</p>
Data for "Multi-band Spectropolarimetric Image Data of Lunar Maria, Pyroclastics, Fresh Craters, and Swirl Materials"
<p>This data repository contains the georeferenced spectropolarimetric data analyzed in the following article:</p> <p>Wöhler, C., Arnaut, M., Bhatt, M., 2024. Multi-band Spectropolarimetry of Lunar Maria, Pyroclastics, Fresh Craters, and Swirl Material. Astronomical Journal, accepted for publication.</p> <p>The data products are available in separate .zip archives in GEOTIFF and BSQ format. Each .zip archive contains data from eight different observations:</p> <table> <tbody> <tr> <td>Dataset</td> <td>Date</td> <td>UT time</td> <td>Phase angle</td> </tr> <tr> <td>20221114_WOP</td> <td>Nov 14th, 2022</td> <td>05:20</td> <td>64°</td> </tr> <tr> <td>20221216_WOP</td> <td>Dec 16th, 2022</td> <td>04:10</td> <td>88°</td> </tr> <tr> <td>20230225_AT</td> <td>Feb 25th, 2023</td> <td>19:20</td> <td>108°</td> </tr> <tr> <td>20230227_AT</td> <td>Feb 27th, 2023</td> <td>21:44</td> <td>85°</td> </tr> <tr> <td>20230228_AT</td> <td>Feb 28th, 2023</td> <td>19:33</td> <td>74°</td> </tr> <tr> <td>20230302_MV</td> <td>Mar 2nd, 2023</td> <td>19:03</td> <td>52°</td> </tr> <tr> <td>20230302_AT</td> <td>Mar 2nd, 2023</td> <td>19:10</td> <td>52°</td> </tr> <tr> <td>20230402_AT</td> <td>Apr 2nd, 2023</td> <td>20:03</td> <td>38°</td> </tr> </tbody> </table> <p> </p> <p>Data products of spectropolarimetric image analysis in GEOTIFF and BSQ format<br>=============================================================================</p> <p>Prefix 1: Date of data acquisition (YYYYMMDD)<br>Prefix 2: Area (WOP: Western Oceanus Procellarum; MV: Mare Vaporum; AT: Atlas)</p> <p>F : image intensity (5 bands) [DN]<br>P : degree of linear polarization (DoLP) (5 bands)<br>W : angle of linear polarization (AoLP) (5 bands) [degrees]<br>GS : relative grain size (5 bands)<br>logGS: logarithm of relative grain size (5 bands)<br>UE : across-band Umov exponent (1 band)<br>UEres: residual of across-band Umov exponent (1 band)<br>PCAP : scores on the first three principal components derived from the DoLP (3 bands) <br>PCAW : scores on the first three principal components derived from the AoLP (3 bands)<br>CIM : cluster index map (1 band)</p> <p><br>The longitude ranges of the maps are as follows:</p> <p>WOP: Longitude=[-70 -35], Latitude=[2 33]<br>MV : Longitude=[-13 12], Latitude=[-2 17]<br>AT : Longitude=[35 60], Latitude=[40 55]</p> <p>All maps are in simple cylindrical projection with a resolution of 30 pixels per degree.</p> <p>The CIM maps are provided in uint8 numerical format.<br>All other maps are provided in single-precision (32-bit) floating point numerical format.<br>The BSQ files are in binary format without header. Matlab example:<br>BSQ=multibandread('20230302_AT_P__Latitude_35_60__Latitude_35_60.bsq',[750 750 5],'single',0,'bsq','ieee-le');</p> <p> </p>
Supporting Material for "Automatic Mapping of Small Lunar Impact Craters Using LROC NAC Images"
<p>The supporting material for <em>'Automatic Mapping of Small Lunar Impact Craters Using LROC NAC'.</em></p> <p>This File contains:</p> <ul> <li>Supporting Material (.pdf);</li> <li>List of True Positive detections (.csv);</li> <li>List of all ground truth and CDA detections (.csv);</li> <li>Folder (.zip) with images of the evaluation sites (.pdf); and</li> <li>Folder (.zip) with training image tiles (.png and .txt).</li> </ul> <p>Refer to Supporting Material (.pdf) for file name and header information.</p>
Internal morphology point clouds of lunar pits
<p>This archive contains point clouds showing the internal geometry of six pits on the Moon. These point clouds were generated via manual feature matching in "oblique stereo pairs": pairs of Lunar Reconnaissance Orbiter Narrow Angle Camera (LROC NAC) images at two different off-nadir angles observing one wall of a pit under similar lighting conditions. These images have pixel scales of ~0.3-2.2 m/pixel, allowing the creation of point clouds with point spacing on the order of 5-10 m, depending on the density of identifiable features on the pit walls.</p> <p>The manually-generated point clouds from multiple stereo pairs have been merged together, and aligned to and merged with dense digital terrain models (DTMs) from more nadir-looking NAC stereo images where available, to produce point clouds that cover the upper walls, floors, and immediate surroundings of the pits.</p> <p>For a full description of the processing method, see Wagner and Robinson (2022), linked in this archive's metadata.</p> <p>This archive contains models for the following pits:<br> Lacus Mortis Pit (LMP)<br> Mare Ingenii Pit (MIP)<br> Mare Tranquillitatis Pit (MTP)<br> Marius Hills Pit (MHP)<br> Schlüter Crater Pit (SCP)<br> Southwest Mare Fecunditatis Pit (SWFP)</p>
Lunar Reconnaissance Orbiter Imagery for LROCNet Moon Classifier
<p><strong>Summary</strong></p> <p>We provide imagery used to train LROCNet -- our Convolutional Neural Network classifier of orbital imagery of the moon. Images are divided into train, validation, and test zip files, which contain class specific sub-folders. We have three classes: "fresh crater", "old crater", and "none". Classes are described in detail in the attached labeling guide.</p> <p><strong>Directory Contents</strong></p> <p>We include the labeling guide and training, testing, and validation data. Training data was split to avoid upload timeouts.</p> <ul> <li>LROC_Labeling_Intro_for_release.ppt: Labeling guide</li> <li>val: Validation images divided into class sub-folders <ul> <li>ejecta: "fresh crater" class</li> <li>oldcrater: "old crater" class</li> <li>none: "none" class</li> </ul> </li> <li>test: Testing images divided into class sub-folders <ul> <li>ejecta: "fresh crater" class</li> <li>oldcrater: "old crater" class</li> <li>none: "none" class</li> </ul> </li> <li>ejecta_train: Training images of "fresh crater" class</li> <li>oldcrater_train: Training images of "old crater" class</li> <li>none_train1-4: Training images of "none" class (divided into 4 just for uploading)</li> </ul> <p><strong>Data Description </strong></p> <p>We use CDR (Calibrated Data Record) browse imagery (50% resolution) from the Lunar Reconnaissance Orbiter's Narrow Angle Cameras (NACs). Data we get from the NACs are 5-km swaths, at nominal orbit, so we perform a saliency detection step to find surface features of interest. A detector developed for Mars HiRISE (Wagstaff et al.) worked well for our purposes, after updating based on LROC NAC image resolution. We use this detector to create a set of image chipouts (small 227x277 cutouts) from the larger image, sampling the lunar globe.</p> <p><strong>Class Labeling</strong></p> <p>We select classes of interest based on what is visible at the NAC resolution, consulting with scientists and performing a literature review. Initially, we have 7 classes: "fresh crater", "old crater", "overlapping craters", "irregular mare patches", "rockfalls and landfalls", "of scientific interest", and "none".</p> <p>Using the Zooniverse platform, we set up a labeling tool and labeled 5,000 images. We found that "fresh crater" make up 11% of the data, "old crater" 18%, with the vast majority "none". Due to limited examples of the other classes, we reduce our initial class set to: "fresh crater" (with impact ejecta), "old crater", and "none".</p> <p>We divide the images into train/validation/test sets making sure no image swaths span multiple sets.</p> <p><strong>Data Augmentation</strong></p> <p>Using PyTorch, we apply the following augmentation on the training set only: horizontal flip, vertical flip, rotation by 90/180/270 degrees, and brightness adjustment (0.5, 2). In addition, we use weighted sampling so that each class is weighted equally. The training set included here does not include augmentation since that was performed within PyTorch.</p> <p><strong>Acknowledgements</strong></p> <p>The author would like to thank the volunteers who provided annotations for this data set, as well as others who contributed to this work (as in the Contributor list). We would also like to thank the PDS Imaging Node for support of this work.</p> <p>The research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (80NM0018D0004).</p> <p>CL#22-4763</p> <p>© 2022 California Institute of Technology. Government sponsorship acknowledged.</p>
Dataset for "Reflectance spectra of seven lunar swirls examined by statistical methods: A space weathering study"
<p>This archive corresponds to the source code, raw data, and results described in the article "Reflectance spectra of seven lunar swirls examined by statistical methods: A space weathering study" by Chrbolková et al. (2019) published in Icarus journal. See AA_README.txt for more information.</p>
Supplemental Data Sets for "Buried Ice Deposits in Lunar Polar Cold Traps were Disrupted by Ballistic Sedimentation"
<p>Supporting Data Sets for manuscript "Buried Ice Deposits in Lunar Polar Cold Traps were Disrupted by Ballistic Sedimentation". Contains Data Sets S1-S7 as described in the manuscript and Supplementary information S1 (see <a href="https://doi.org/10.1029/2022JE007567">https://doi.org/10.1029/2022JE007567</a>).</p>
Datasets for Wohlfarth et al. (2023) An advanced thermal roughness model for airless planetary bodies - Implications for global variations of lunar hydration and mineralogical mapping of Mercury with the MERTIS spectrometer
<p>This document describes the datasets and modeling results presented and discussed in our full research article.<br> <br> Wohlfarth, K., Wöhler, C., Hiesinger, H., Helbert, J. 2023, An advanced thermal roughness model for airless planetary bodies - Implications for global variations of lunar hydration and mineralogical mapping of Mercury with the MERTIS spectrometer, Astronomy and Astrophysics, 672<br> <br> <a href="https://doi.org/10.1051/0004-6361/202245343">https://doi.org/10.1051/0004-6361/202245343</a><br> <br> We provide several visualization scripts that read and display the results for convenience. Access to the original MATLAB® code for the thermal model implementation is available upon request (<a href="mailto:kay.wohlfarth@tu-dortmund.de">kay.wohlfarth@tu-dortmund.de</a>).<br> <br> More info in Dataproducts.pdf</p>
Recalibration of the lunar chronology due to spatial cratering-rate variability - Data and code
<ul> <li>CR_moon.csv: Relative cratering rate shown in Fig.2. The<strong> </strong>data are provided over the full range of latitudes and longitudes, with a 1-degree bin.</li> <li>cr_lefeuvre2011.txt: Relative cratering rate proposed by Le Feuvre and Wieczorek (2011). The<strong> </strong>data are provided over the full range of latitudes and longitudes, with a 1-degree bin.</li> <li>lunar_calib_points.csv: Table summarising the lunar chronology calibration points used in this study. </li> <li>Lagain_AA_convert_age.m: Matlab code converting model ages of Plutarch and Kirkwood craters from Neukum et al. (2001) chronology into the one presented in this study. The code also computes the chronology model from Le Feuvre and Wieczorek (2011) and the one presented in this study for different locations, and compares it with the Neukum et al. (2001) chronology.</li> </ul>
Sagittarius A lunar occultation measured by Dwingeloo Radio Telescope 2023-12-13
<p>This dataset contains raw spectra from the Lunar occultation of Sagittarius A on December 13, 2023. The data were obtained with the Dwingeloo Radio Telescope, which is operated by Stichting CAMRAS.</p> <p>Three bands are measured:</p> <ul> <li>1418MHz, 6MHz wide. The spectra show absorption and emission of the hydrogen line.<br> <ul> <li>Spectra with 2000 bins, integration time 0.2 seconds (corrected for bandpass).</li> <li>Total power with integration time 1 second.</li> </ul> </li> <li>1330MHz, 10MHz wide. <ul> <li>Spectra with 5000 bins, integration time 1 second.</li> <li>Total power with integration time 1 second.</li> </ul> </li> <li>415MHz, 4MHz wide. This data is severely affected by radio frequency interference.<br> <ul> <li>Spectra with 2000 bins, integration time 1 second.</li> <li>Total power with integration time 1 second.</li> </ul> </li> </ul> <p>The telescope was tracking Sagittarius A* during the occultation. The altitude ranged from 1 to 8 degrees above the horizon during the measurements. This is very low, leading to quite severe radio frequency interference. Partially, these are caused by LTE masts transmitting at 1456 MHz, for which the receiving system is insufficiently shielded. All frequencies are topocentric, i.e. no LSR-correction has been applied.</p> <p>Files are in the ECSV format, which can be read with Astropy, or with any other program that can read CSV-data (such as Microsoft Excel). Apart from the spectra, also telescope pointing information and exact times are stored in every row.</p>
Iron-rich grain-decorated Depressions on Surfaces of Lunar Impact Glasses
<p>The dataset includes the semi-quantitative values and EDS spectra of constitutions of iron-rich grain-decorated depressions on surfaces of Chang'E-5 impact glass particles. Semi-quantitative values were classified by ultra-thin sections, and EDS spectra were marked by sampling sites.</p>
Data repository for "Genesis and timing of KREEP-free lunar Mg-suite magmatism indicated by the first norite meteorite Arguin 002"
<p>This is the data repository for paper entitled "Genesis and timing of KREEP-free lunar Mg-suite magmatism indicated by the first norite meteorite Arguin 002". This data repository includes two EXCEL (.xlsx) files representing the dataset necessary to interpret, replicate and build upon the methods or findings reported in the article.</p> <p>Regarding the EXCEL file named "Supplementary Data 1", it incorporates the mineral EPMA compositions (Table S1), mineral trace-element compositions (Table S2), bulk chemistry (Table S3), SIMS U-Pb results (Table S4), and mineral modal abundances and the launch-region identification results (Table S5) in the comprehensive study of the lunar norite meteorite, Arguin 002.</p> <p>Regarding the EXCEL file named "Supplementary Data 2", it incorporates analyses of reference materials in EPMA (Table S1), LA-ICP-MS (Table S2), ICP-MS (Table S3), and SIMS (Table S4).</p>
Dataset for ´´A New Detailed Global Map of Lunar Light Plains´´ research article
<p>The shapefiles (.shp) provided in this repository are the datasets for the paper ´A new detailed global map of lunar light plains´ published in PSJ journal Special Issue. </p> <p>These shapefiles can be directly imported in ArcMap/ArcPRO. The third dataset is a .tif or image of the global map for a fast and easy overview.</p> <p>Two geomorphologic maps of lunar light plains are provided as described in the article: one with an FeO wt% cut off of about 12 wt% (Area_lightplains), and the other around 8 wt% (Area_LPFeOLow). </p> <p> </p>
Testing the Fidelity of Paleopole Determinations from Multidirectionally Magnetized Lunar Crustal Anomaly Source Bodies
<p>Code and data for "Testing the Fidelity of Paleopole Determinations from Multidirectionally Magnetized Lunar Crustal Anomaly Source Bodies ". See readme.txt</p>
Global mapping of lunar refractory elements: multivariate regression vs. machine learning
<p>The quantitative estimation of elemental concentrations at the spatial resolution of hyperspectral near-infrared (NIR) images<br> of the lunar surface is an important tool for understanding the processes relevant for the origin and evolution of the Moon. The NIR reflectance of the lunar regolith is an integrated response to the presence of refractory elements and soil alteration processes. Our approach was to define a combination of spectral parameters that are robust with respect to the effects of soil maturity.<br> We calibrated the spectral parameters with respect to elemental abundances measured by the Lunar Prospector Gamma Ray Spectrometer (LP GRS) and the Kaguya GRS (KGRS). For this purpose, we compared a classical multivariate linear regression (MLR) approach and the machine learning based support vector regression (SVR) technique applied to M3 global observations. The M 3 -based global elemental maps are consistent in distribution and range with the LP GRS and KGRS elemental maps<br> and do not show artifacts in immature areas such as small fresh craters. The results derived using MLR and SVR are compared to<br> sample-based ground truth data of the Apollo and Luna sample-return sites, where the root-mean-square deviations obtained by the<br> two regression models are similar. The main advantage of the proposed new algorithm is its ability to minimize artifacts due to space-weathering effects. The elemental maps of Mg and Ca provide additional information and reveal structures not always visible in the Fe map. The global elemental abundance maps derived for the fully calibrated M 3 observations might thus serve as important tools to investigate the lunar geology and evolution.</p>
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