Temperature dependent mid-infrared (5–25 μm) reflectance spectroscopy of carbonaceous meteorites and minerals: Implication for remote sensing in Solar System exploration

Icarus ◽  
2021 ◽  
Vol 354 ◽  
pp. 114040
Author(s):  
Giovanni Poggiali ◽  
John Robert Brucato ◽  
Elisabetta Dotto ◽  
Simone Ieva ◽  
Maria Antonietta Barucci ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Solar System ◽  
Reflectance Spectroscopy ◽  
Temperature Dependent ◽  
Mid Infrared ◽  
Solar System Exploration

Temperature dependent mid-infrared (5-25 μm) reflectance spectroscopy of carbonaceous meteorites and minerals: implication for remote sensing in Solar System exploration.

2020 ◽  
Author(s):  
Giovanni Poggiali ◽  
John Robert Brucato ◽  
Elisabetta Dotto ◽  
Simone Ieva ◽  
Maria Antonietta Barucci ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Grain Size ◽  
Spectral Range ◽  
Peak Position ◽  
Space Environment ◽  
Particle Sizes ◽  
Temperature Dependent ◽  
Geophysical Research ◽  
Mid Infrared ◽  
Band Area

<p>Interpretation of spectroscopic data from remote sensing strongly depends on the spectroscopic properties, particle size and temperature of materials on the observed surface. Spectral indices of silicates, carbonates, sulfates, oxides and chemicals available on public database are commonly obtained at room temperature and pressure. Whether temperature can affect spectral properties of minerals such as the peak position, band area and shape, was advanced decades ago (Singer & Roush, 1985), but a systematic laboratory study on such effects is still missing. Hitherto, few studies were performed analyzing the effects of space environment, such as low pressure and temperature, on spectroscopic features of minerals, mostly focusing on near infrared spectral region (Moroz, et al. 2000; Hinrichs & Lucey 2002; De Angelis, et al. 2019). This is especially lacking in the mid-infrared region, where laboratory data are almost completely absent. Thus, it is pivotal to acquire spectra in vacuum at various temperatures and varying the particle sizes, for better simulating space environmental conditions.</p><p>Our apparatus at INAF-Astrophysical Observatory of Arcetri allows reflectance measurements in an extended spectral range from VIS to far IR and at temperatures ranging from 64 K to 500 K. We present here a detailed analysis on temperature-dependent variation on mineral and carbonaceous chondrite samples in the spectral range 1500-400 cm<sup>-1</sup> (6.6-25 µm in wavelength). Mineral phases and meteorites analyzed are: pyroxene, olivine, serpentine, Tagish Lake (CI2-ungruped), Aguas Zarcas (CM2) and Orgueil (CI1). Samples are prepared with particle sizes <20 μm, <200 μm, and 200-500 μm. Our results show that temperature induces spectral features modifications such as peak position shifts, band area and peak intensity changes (Fig 1). Modifications are reversible with temperature and the trend of variation is related to the sample composition and hydration level. Moreover, magnitude of temperature-dependent spectroscopic changes is strongly linked with grain size and composition, hence making this type of analysis pivotal for a correct interpretation of data collected by space telescopes and orbital spacecrafts.</p><p> </p><p><img src="https://contentmanager.copernicus.org/fileStorageProxy.php?f=gnp.b99a2d2ef2fe55803892951/sdaolpUECMynit/0202CSPE&app=m&a=0&c=eb4e74f18ee6e81cf082f62ce54b5556&ct=x&pn=gnp.elif" alt=""></p><p><em><strong>Figure 1</strong> Meteorite spectra at different temperature normalized at 1500 cm<sup>-1</sup> in wavenumber range between 1500 cm<sup>-1</sup> and 400 cm<sup>-1</sup>. Tagish Lake (top left panel), Orgueil (top right panel) and Aguas Zarcas (bottom left panel). All samples are sieved in grain size 200-500 µm. Spectra were acquired at different temperature step from 65 K (light blue) to 350 K (red). Position of Christiansen features (CF) and Reststrahlen bands (RB) are highlighted.</em></p><p> </p><p><strong>References<br></strong>De Angelis, S., et al. 2019. Icarus, 317, 388-411<br>Hinrichs, J. L. & Lucey, P. G., 2002. Icarus, 155, 169-180<br>Moroz, L., Schade, U. & Wash, R., 2000. Icarus, 147, 79-93.<br>Singer, R. B. & Roush, T. L., 1985. Journal of Geophysical Research, 90 (B14), 12434-12444.</p>

Temperature‐dependent Formation of Ozone in Solid Oxygen by 5 keV Electron Irradiation and Implications for Solar System Ices

2007 ◽  
Vol 669 (2) ◽  
pp. 1414-1421 ◽  
Author(s):  
Bhalamurugan Sivaraman ◽  
Corey S. Jamieson ◽  
Nigel J. Mason ◽  
Ralf I. Kaiser
Keyword(s):  
Solar System ◽  
Electron Irradiation ◽  
Temperature Dependent ◽  
Solid Oxygen

scholarly journals Extraterrestrial Influences on Remote Sensing in the Earth’s Atmosphere

2021 ◽  
Vol 13 (5) ◽  
pp. 890
Author(s):  
Aleksandra Nina ◽  
Milan Radovanović ◽  
Luka Č. Popović
Keyword(s):  
Remote Sensing ◽  
Solar System ◽  
Solar Radiation ◽  
Significant Influence ◽  
Coming Out ◽  
Outer Space ◽  
Signal Propagation ◽  
Atmospheric Parameters ◽  
Atmospheric Disturbances ◽  
Propagation Paths

Atmospheric properties have a significant influence on electromagnetic (EM) waves, including the propagation of EM signals used for remote sensing. For this reason, changes in the received amplitudes and phases of these signals can be used for the detection of the atmospheric disturbances and, consequently, for their investigation. Some of the most important sources of the temporal and space variations in the atmospheric parameters come from the outer space. Although the solar radiation dominates in these processes, radiation coming out of the solar system also can induces enough intensive disturbance in the atmosphere to provide deflections in the EM signal propagation paths. The aim of this issue is to present the latest research linking events and processes in outer space with changes in the propagation of the satellite and ground-based signals used in remote sensing.

Using absorbance peak of carbonate to select suitable regression model before predicting soil inorganic carbon concentration by mid-infrared reflectance spectroscopy

2021 ◽  
Author(s):  
Cécile Gomez ◽  
Tiphaine Chevallier ◽  
Patricia Moulin ◽  
Bernard G. Barthès
Keyword(s):  
Prediction Model ◽  
Regression Models ◽  
Inorganic Carbon ◽  
Reflectance Spectroscopy ◽  
Soil Samples ◽  
Infrared Reflectance ◽  
Mid Infrared ◽  
Soil Inorganic Carbon ◽  
Infrared Reflectance Spectroscopy ◽  
Absorbance Peak

<p><span>Mid-Infrared reflectance spectroscopy (MIRS, 4000 – 400 cm<sup>-1</sup>) is being considered to provide accurate estimations of soil inorganic carbon (SIC) contents. Usually, the prediction performances by MIRS are analyzed using figures of merit based on entire test datasets characterized by large SIC ranges, without paying attention to performances at sub-range scales. This work aims to <em>1)</em> evaluate the performances of MIR regression models for SIC prediction, for a large range of SIC test data (0-100 g/kg) and for several regular sub-ranges of SIC values (0-5, 5-10, 10-15 g/kg, etc.) and <em>2)</em> adapt the prediction model depending on sub-ranges of test samples, using the absorbance peak at 2510 cm<sup>-1</sup> for separating SIC-poor and SIC-rich test samples. This study used a Tunisian MIRS topsoil dataset including 96 soil samples, mostly rich in SIC, to calibrate and validate SIC prediction models; and a French MIRS topsoil dataset including 2178 soil samples, mostly poor in SIC, to test them. Two following regression models were used: a partial least squares regression (PLSR) using the entire spectra and a simple linear regression (SLR) using the height of the carbonate absorbance peak at 2150 cm<sup>-1</sup>.</span></p><p><span>First, our results showed that PLSR provided <em>1) </em>better performances than SLR on the Validation Tunisian dataset (R<sup>2</sup><sub>test</sub> of 0.99 vs. 0.86, respectively), but <em>2) </em>lower performances than SLR on the Test French dataset (R<sup>2</sup><sub>test</sub> of 0.70 vs. 0.91, respectively). Secondly, our results showed that on the Test French dataset, predicted SIC values were more accurate for SIC-poor samples (< 15 g/kg) with SLR (RMSE<sub>test</sub> from 1.5 to 7.1 g/kg, depending on the sub-range) than with PLSR prediction model (RMSE<sub>test </sub>from 7.3 to 14.8 g/kg, depending on the sub-range). Conversely, predicted SIC values were more accurate for carbonated samples (> 15 g/kg) with PLSR (RMSE<sub>test</sub> from 4.4 to 10.1 g/kg, depending on the sub-range) than with SLR prediction model (RMSE<sub>test</sub> from 6.8 to 14 g/kg, depending on the sub-range). Finally, our results showed that the absorbance peak at 2150 cm<sup>-1</sup> could be used before prediction to separate SIC-poor and SIC-rich test samples (452 and 1726 samples, respectevely). The SLR and PLSR regression methods applied to these SIC-poor and SIC-rich test samples, respectively, provided better prediction performances (<em>R²</em><sub><em>test </em></sub>of 0.95 and <em>RMSE</em><sub><em>test</em></sub> of 3.7 g/kg<sup></sup>). </span></p><p><span>Finally, this study demonstrated that the use of the spectral absorbance peak at 2150 cm<sup>-1</sup> provided useful information on Test samples and helped the selection of the optimal prediction model depending on SIC level, when using calibration and test sample sets with very different SIC distributions.</span></p>

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