scholarly journals Investigating the Retention of Solar Wind Implanted Helium-3 on the Moon from the Analysis of Multi-Wavelength Remote Sensing Data

2020 ◽  
Vol 12 (20) ◽  
pp. 3350
Author(s):  
Shashwat Shukla ◽  
Valentyn Tolpekin ◽  
Shashi Kumar ◽  
Alfred Stein
Keyword(s):  
Remote Sensing ◽  
Solar Wind ◽  
Remote Sensing Data ◽  
Physical Nature ◽  
The Moon ◽  
Near Surface ◽  
Energy Demands ◽  
Multi Wavelength ◽  
High Dielectric ◽  
Weathering Effects

The Moon has a large potential for space exploration and mining valuable resources. In particular, 3He provides rich sources of non-radioactive fusion fuel to fulfill cislunar and Earth’s energy demands, if found economically feasible. The present study focuses on developing advanced techniques to prospect 3He resources on the Moon from multi-sensor remote sensing perspectives. It characterizes optical changes in regolith materials due to space weathering as a new retention parameter and introduces a novel machine learning inversion model for retrieving the physical properties of the regolith. Our analysis suggests that the reddening of the soil predominantly governs the retention, along with attenuated mafic band depths. Moreover, semi-variograms show that the spatial variability of 3He is aligned with the episodic weathering events at different timescales. We also observed that pyroclastic regoliths with high dielectric constant and increased surface scattering mechanisms exhibited a 3He abundant region. For ejecta cover, the retention was weakly associated with the dielectric contrast and a circular polarization ratio (CPR), mainly because of the 3He-deficient nature of the regolith. Furthermore, cross-variograms revealed inherent cyclicity attributed to the sequential process of weathering effects. Our study provides new insights into the physical nature and near-surface alterations of lunar regoliths that influence the spatial distribution and retention of solar wind implanted 3He.

Anomalous Dielectric Variability in the central peak of Tycho crater on the Moon: New insights from Mini-RF S-band Observations

2021 ◽  
Author(s):  
Shashwat Shukla ◽  
Gerald Wesley Patterson
Keyword(s):  
Dielectric Constant ◽  
Hyperspectral Image ◽  
Central Peak ◽  
Skin Depth ◽  
The Moon ◽  
Near Surface ◽  
Low Dielectric ◽  
Crater Morphology ◽  
High Dielectric ◽  
The Impact

<p>One of the unique candidates to explore the evolution of physical surface processes on the Moon is Tycho, a dark haloed impact crater representing well-preserved bright ray pattern and intact crater morphology. Sampling of the central peak in such complex crater formation proves significant in terms of unraveling intriguing science of the lunar interior. With the current state-of-the-art radar technology, it is possible to evaluate the response of the geologic features constrained in the near surface and subsurface regolith environments. This can be achieved by modelling the dielectric constant of media, which is a physical parameter crucial for furthering our knowledge about the distribution of materials within different stratigraphic layers at multiple depths. Here, we used the applicability of Mini-RF S-band data augmented with a deep learning based inversion model to retrieve the dielectric variations over the central peak of the Tycho crater. A striking observation is made in certain regions of the central peak, wherein we observe anomalously high dielectric constant, not at all differentiated in the hyperspectral image and first Stokes parameter image, which usually is a representation of retrieved backscatter of the target. The results are also supported by comparing the variations in the scattering mechanisms. We found those particular regions to be associated with high degree of depolarization, thereby attributing to the presence of cm- to m- scale scatterers buried within a low dielectric layer that are not big enough to produce even-bounce geometry for the radar wave. Moreover, we also observe high rock concentration in the central peak slopes from DIVINER data and NAC images, indicating the exposure of clasts ranging in size from 10 meter to 100s of meter. Furthermore, from surface temperature data, these distinctive outcrops sense warmer temperature at night than the surrounding, which suggests the existence of thermal skin depth in such vicinities. Interestingly, we are able to quantify the pessimistic dielectric constant limit of the large boulder in the middle of the central peak, observable at the Mini-RF radar wavelength, as 4.54 + j0.077. Compared to the expected dielectric constant of rocks, this value is lowered significantly. One probable reason could be the emergence of small radar shadows due to the rugged surface of the boulder on the radar illuminated portion. From our analysis, we showcase the anomalous dielectric variability of Tycho central peak, thereby providing new insights into the evolution of the impact cratering process that could be important for both science and necessary for framing human or robotic exploration strategies.  </p>

Retrieval of Hourly Records of Surface Hydrometeorological Variables Using Satellite Remote Sensing Data

2015 ◽  
Vol 16 (1) ◽  
pp. 147-157 ◽  
Author(s):  
Sanaz Moghim ◽  
Andrew Jay Bowen ◽  
Sepideh Sarachi ◽  
Jingfeng Wang
Keyword(s):  
Remote Sensing ◽  
Air Temperature ◽  
Sensible Heat Flux ◽  
Surface Air Temperature ◽  
Remote Sensing Data ◽  
Heat Fluxes ◽  
Production Model ◽  
Integral Model ◽  
Near Surface ◽  
Surface Heat Fluxes

Abstract A new algorithm is formulated for retrieving hourly time series of surface hydrometeorological variables including net radiation, sensible heat flux, and near-surface air temperature aided by hourly visible images from the Geostationary Operational Environmental Satellite (GOES) and in situ observations of mean daily air temperature. The algorithm is based on two unconventional, recently developed methods: the maximum entropy production model of surface heat fluxes and the half-order derivative–integral model that has been tested previously. The close agreement between the retrieved hourly variables using remotely sensed input and the corresponding field observations indicates that this algorithm is an effective tool in remote sensing of the earth system.

Solar wind interaction with the lunar surface: Observation of energetic neutral atoms on the lunar surface by the Advanced Small Analyzer for Neutrals (ASAN) instrument on the Yutu-2 rover of Chang'E-4.

2020 ◽  
Author(s):  
Martin Wieser ◽  
Stas Barabash ◽  
Xiao-Dong Wang ◽  
Aibing Zhang ◽  
Chi Wang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Solar Wind ◽  
Lunar Surface ◽  
Lunar Regolith ◽  
Remote Sensing Data ◽  
High Porosity ◽  
Neutral Atoms ◽  
Solar Wind Interaction ◽  
Energetic Neutral Atoms ◽  
Nominal Performance

<p>A fraction of up to 20% of the solar wind impinging onto the lunar surface is reflected as energetic neutral atoms back to space, as established by remote sensing, e.g. by the SARA instrument on Chandrayaan-1 or by IBEX. Mapping of these reflected energetic neutral atoms to the surface opened a new way to remotely study the solar wind precipitation onto the surface. However, the high reflection rate remained an enigma given the high porosity of the lunar regolith, but no measurements directly on the surface were available.</p><p>With the Advanced Small Analyzer for Neutrals (ASAN) mounted on the Yuyu-2 the rover of Chang'E-4, for the first time measurements of the energetic neutral atom flux originating from the lunar surface were preformed directly on the lunar surface itself. ASAN measures with a single angular pixel the energy spectrum of energetic neutral atoms reflected or sputtered form the surface with coarse mass resolution. ASAN uses the mobility of the rover to cover different solar wind illumination angles and scattering angles from the surface.</p><p>Since the landing of Chang'E-4 in the Von Kármán crater on the lunar far side in January 2019, ASAN has spent more than one year on the lunar surface and performed typically two measurement sessions per lunar day with nominal performance.</p><p>We review the ASAN instrument status and operations; present energy and mass spectra of energetic neutral atoms backscattered and sputtered from the surface, and discuss sputtering yields observed during different observation sessions. We put these observations into context of earlier remote sensing data by the SARA instrument on Chandrayaan-1.</p>

scholarly journals An Evaluation of Satellite Remote Sensing Data Products for Land Surface Hydrology: Atmospheric Infrared Sounder*

2010 ◽  
Vol 11 (6) ◽  
pp. 1234-1262 ◽  
Author(s):  
Craig R. Ferguson ◽  
Eric F. Wood
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Remote Sensing Data ◽  
Specific Humidity ◽  
Retrieval Algorithm ◽  
Climatic Data ◽  
Atmospheric Infrared Sounder ◽  
Retrieval Models ◽  
Near Surface

Abstract The skill of instantaneous Atmospheric Infrared Sounder (AIRS) retrieved near-surface meteorology, including surface skin temperature (Ts), air temperature (Ta), specific humidity (q), and relative humidity (RH), as well as model-derived surface pressure (Psurf) and 10-m wind speed (w), is evaluated using collocated National Climatic Data Center (NCDC) in situ observations, offline data from the North American Land Data Assimilation System (NLDAS), and geostationary remote sensing (RS) data from the Spinning Enhanced Visible and Infrared Imager (SEVIRI). Such data are needed for RS-based water cycle monitoring in areas without readily available in situ data. The study is conducted over the continental United States and Africa for a period of more than 6 years (2002–08). For both regions, it provides for the first time the geographic distribution of AIRS retrieval performance. Through conditional sampling, attribution of retrieval errors to scene atmospheric and surface conditions is performed. The findings support previous assertions that performance degrades with cloud fraction and that (positive) bias enhances with altitude. In general AIRS is biased warm and dry. In certain regions, strong AIRS–NCDC correlation suggests that bias-driven errors, which can be substantial, are correctable. The utility of the error characteristics for prescribing the input-induced uncertainty of RS retrieval models is demonstrated through two applications: a microwave soil moisture retrieval algorithm and the Penman–Monteith evapotranspiration model. An important side benefit of this study is the verification of NLDAS forcing.

scholarly journals Evaluating sources of an apparent cold bias in MODIS land surface temperatures in the St. Elias Mountains, Yukon, Canada

2021 ◽  
Author(s):  
Ingalise Kindstedt ◽  
Kristin Schild ◽  
Dominic Winski ◽  
Karl Kreutz ◽  
Luke Copland ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Land Surface ◽  
Remote Sensing Data ◽  
Cold Bias ◽  
Near Surface ◽  
Wind Speeds ◽  
Brightness Temperatures ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Temperature Inversions

Abstract. Remote sensing data are a crucial tool for monitoring climatological changes and glacier response in areas inaccessible for in situ measurements. The Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) product provides temperature data for remote glaciated areas where weather stations are sparse or absent, such as the St. Elias Mountains (Yukon, Canada). However, MODIS LSTs in the St. Elias Mountains have shown a cold bias relative to available weather station measurements, the source of which is unknown. Here, we show that the MODIS cold bias likely results from the occurrence of near-surface temperature inversions rather than from the MODIS sensor’s large footprint size or from poorly constrained snow emissivity values used in LST calculations. We find that a cold bias in remote sensing temperatures is present not only in MODIS LST products, but also in Advanced Spaceborne Thermal Emissions Radiometer (ASTER) and Landsat surface temperature products, both of which have a much smaller footprint (90–120 m) than MODIS (1 km). In all three datasets, the cold bias was most pronounced in the winter (mean cold bias > 8 °C), and least pronounced in the spring and summer (mean cold bias < 2 °C). We also find this enhanced seasonal bias in MODIS brightness temperatures, before the incorporation of snow surface emissivity into the LST calculation. Finally, we find the MODIS cold bias to be consistent in magnitude and seasonal distribution with modeled temperature inversions, and to be most pronounced under conditions that facilitate near-surface inversions, namely low incoming solar radiation and wind speeds, at study sites Icefield Divide (60.68° N, 139.78° W, 2,603 m a.s.l) and Eclipse Icefield (60.84° N, 139.84° W, 3,017 m a.s.l.). These results demonstrate that efforts to improve the accuracy of MODIS LSTs should focus on understanding near-surface physical processes rather than refining the MODIS sensor or LST algorithm. In the absence of a physical correction for the cold bias, we apply a statistical correction, enabling the use of mean annual MODIS LSTs to qualitatively and quantitatively examine temperatures in the St. Elias Mountains and their relationship to melt and mass balance.

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