The effect of particle size and porosity on spectral contrast in the mid-infrared

doi:10.1016/0019-1035(85)90078-8

Icarus

Volume 64, Issue 3, December 1985, Pages 586-588

https://doi.org/10.1016/0019-1035(85)90078-8 Get rights and content

Abstract

Contrary to previous work, we find that the decreasing intensity of fundamental molecular vibration bands with decreasing particle size is due primarily to increasing porosity of the finer particle size ranges, rather than to particle size per se. This implies that laser reflectance measurements from orbiting spacecraft should avoid loss of spectral contrast for fine particulate surfaces, because such measurements near zero phase angle will benefit from the opposition effect.

References (14)

R.K. Vincent et al.
Thermal-infrared spectra and chemical analyses of twenty-six igneous rock samples
Remote Sens. Environ.
(1975)
J.E. Conel
Infrared emissivities of silicates: Experimental results and a cloudy atmosphere model of spectral emission from condensed particulate mediums
J. Geophys. Res.
(1969)
R.W. Hannah et al.
Diffuse reflectance using infrared dispersive spectrophotometers
Appl. Spectrosc.
(1983)
B. Hapke
Bidirectional reflectance spectroscopy. 1. Theory
J. Geophys. Res.
(1981)
W.A. Hovis et al.
Infrared reflectance spectra of igneous rocks, tuffs, and red sandstone from 0.5 to 22 μm
J. Opt. Soc. Amer.
(1966)
G.R. Hunt et al.
Mid-Infrared Spectral Behavior of Igneous Rocks
G.R. Hunt et al.
Mid-Infrared Spectral Behavior of Sedimentary Rocks

There are more references available in the full text version of this article.

Cited by (108)

Lithological mapping of charnockites using spectral mixture analysis
2024, Remote Sensing Applications: Society and Environment
Charnockite, an orthopyroxene-bearing granitic rock, is the most dominant rock type of the Southern Granulite Terrain (SGT) of India. This study utilises remote sensing data to map the charnockite lithology in the Kodaikanal region within the SGT. Although remote sensing-based lithological mapping dominantly uses VNIR (Visible-Near Infrared) datasets, in this study, PRISMA (hyperspectral) datasets in the VNIR region and ASTER (multispectral) datasets in the TIR (Thermal Infrared) region were utilised in deriving the mineralogical composition and spectral characteristics of charnockites. In addition, image-derived endmember spectra from PRISMA and ASTER were validated using the laboratory-acquired spectra of charnockites. Linear mixing models (LMM) and the Hapke model were applied to quantitatively study the fractional abundance of charnockite rocks in the Kodaikanal region. Three least square methods, i.e., Fully Constrained Least Squares (FCLS), Non-Negative Least Squares (NNLS), and Unconstrained Least Squares (UCLS), were used to derive the fractional abundance of endmembers. Laboratory-derived charnockite spectra in the VNIR region exhibit spectral features characteristics of pyroxenes and hydrated minerals. In the TIR region, charnockite spectra exhibit characteristic vibrational absorption features of quartz and feldspar known as reststrahlen bands. Image analysis indicates that charnockite rocks exhibit spectral characteristics resembling the mafic rock type in the VNIR region and the felsic rock type in the TIR region. Therefore, the combined analysis of VNIR and TIR datasets allows the accurate delineation of charnockite lithology. However, vegetation in the study area introduces strong nonlinear spectral mixing effects, making it challenging to map the lithological units accurately. This study addresses this challenge using spectral mixture analysis to map charnockites in the Kodaikanal region. The fractional abundance study provides valuable insights into the lithological composition, demonstrating the effectiveness of spectral mixture analysis in mapping charnockites in the Kodaikanal region. Overall, the study demonstrates that the spectral mixture analysis significantly enhances the accuracy of lithology mapping, contributing to a more comprehensive understanding of the study area.
Reflection, emission, and polarization properties of surfaces made of hyperfine grains, and implications for the nature of primitive small bodies
2023, Icarus
Solar System small bodies were the first objects to accrete inside the protoplanetary disk, giving insights into its composition and structure. The P-/D-type asteroids are particularly interesting because of the similarity of their spectra, at visible and near infrared wavelengths (Vis-NIR), with cometary nuclei, suggesting that they are the most primitive types of small bodies. There are various indications that (1) their low albedo in the visible (Vis) and mid-infrared (MIR) wavelength ranges seems mainly controlled by the presence of opaque minerals (iron sulfides, FeNi alloys etc.) (Quirico et al., 2016; Rousseau et al., 2018); and (2) their surfaces are made of intimate mixtures of these opaque minerals and other components (silicates, carbonaceous compounds, etc.) in the form of sub-micrometre-sized grains, smaller than the wavelength at which they are observed, so-called “hyperfine” grains. Here, we investigate how the Vis-NIR-MIR (0.55–25 μm) spectral and V-band (0.53 μm) polarimetric properties of surfaces made of hyperfine grains are influenced by the relative abundance of such hyperfine materials, having strongly different optical indexes. Mixtures of grains of olivine and iron sulfide (or anthracite), as analogues of silicates and opaque minerals present on small bodies, were prepared at different proportions. The measurements reveal that these mixtures of hyperfine grains have spectral and polarimetric Vis-NIR properties varying in strongly nonlinear ways. When present at even a few percent, opaque components dominate the Vis-NIR spectral and polarimetric properties, and mask the silicate bands at these wavelengths. The Vis-NIR spectral slope ranges from red (positive slope), for pure opaque material, to blue (negative slope) as the proportion of silicates increases, which is reminiscent of the range of spectral slopes observed on P/D/X/C- and B-types asteroids. The spectra of the darkest mixtures in the Vis-NIR exhibit the absorption bands of SiO in olivine around 10 μm in the MIR, which is observed in emission for several small bodies. The samples studied here have macro- and micro-porosities lower than 78%, indicating that surfaces more compact than “fairy castle” hyperporous (80–99%) ones can also exhibit a blue spectral slope or a silicate signature at 10 μm. Remarkably, some mixtures exhibit altogether a red spectral slope in the Vis-NIR, a 10-μm feature in the MIR, and a V-band polarimetric phase curve similar (but not identical) to P-/D-type asteroids, reinforcing the hypothesis that these bodies are made of powdery mixtures of sub-micrometre-sized grains having contrasted optical indexes. This work shows that both the contrasted optical indexes of the components, and the dispersion or aggregation −depending on their relative proportions− of their hyperfine grains, induce different light scattering regimes in the Vis-NIR and MIR, as observed for primitive small bodies. The optical separation of hyperfine grains seems to be a major parameter controlling the optical properties of these objects.
Imaging geomechanical properties of shales with infrared light
2023, Fuel
Hyperspectral images are data cubes that associate with each pixel an infrared (IR) reflectance spectrum. We hypothesize there is an implicit but discernable link between IR spectra of rocks and their mechanical response that can be learned by a machine learning (ML) algorithm. Our rationale is that IR spectra are sensitive to both rock composition and microstructure, two attributes that determine all hydro-thermo-mechanical properties of the rock. To test this hypothesis, we confine ourselves to shale specimens from the Green River formation, Utah, US, and aim to map acoustic velocity (V_p), acoustic attenuation coefficient (A_p), and X-ray attenuation coefficient (µ) from hyperspectral images acquired in the near-, shortwave-, and mid-IR. We propose an experimental workflow that generates large quantities of labelled data needed to train supervised ML algorithms. The trained algorithms are shown to accurately predict V_p, A_p, and µ directly from pixelwise IR spectra. In some cases, even a 1% random sampling of pixels is found sufficient for training. While both artificial and convolutional neural networks are probed, performances are similar, but better than linear regression. More important is ensuring the training data are representative of all lithologies to be targeted during prediction. This work implies the feasibility of extrapolating beyond a handful of point or line measurements from indentation, scratch testing, or field surveys to, respectively, cm-scale, m-scale, and potentially outcrop-scale rock faces.
Lithology mapping with satellite images, fieldwork-based spectral data, and machine learning algorithms: The case study of Beiras Group (Central Portugal)
2023, Catena
Citation Excerpt :
The wavelength interval in these spectra varies between 0.4 and 15.4 nm (Baldridge et al., 2009), depending on the required minerals to be analysed. Various granulometric fractions are used for the mineralogical spectral analysis, such as (i) 125–500 μm; (ii) 45 μm; and (iii) < 45 μm, to show the granulometry effect in the obtained SR (Hunt & Vincent, 1968; Salisbury & Eastes, 1985). The present study aims to interpret geological units’ mineral spectral response using spectral sensors like Landsat 8 Operational Land Imager (OLI), a spectroradiometer, and combining field investigations.
Geological remote sensing has been an invaluable means to obtain data to perform geological mapping objectively and with high accuracy. However, there is a significant gap in geological cartography information at 1:50 000 scale throughout the territory in mainland Portugal. The lack of geological mapping is reflected in the geological resources and land management information. This investigation’s main objective was to assess the viability of using remote sensing, machine learning and geochemical techniques as proof of concept for the Portuguese context using the Beiras Group (mainland Portugal) as a case study area of 341 km². Multispectral analysis was carried out in two Landsat-8 images of 2015, one in the winter and the other in the summer. Hyperspectral data were obtained using a 400–1000 nm spectroradiometer applied to 23 rock samples collected in two field campaigns – the first in January and the second in April 2019. Spectral differences were found distinguishing the two main lithological units, where the granites (Granito de Coentral and Granito de Vila Nova) had an increasing wavelength spectra shape throughout the whole VNIR measurements. Geochemical data was carried out using X-ray Fluorescence, where the average quantity of major elements such as Na₂O [2.15 %] and CaO [0.41 %] was higher in granites than metasediments: 0.38 % and 0.11 %, respectively. The J48 machine learning algorithm was performed using as input Landsat-8 reflectance data which showed a high success rate in both confusion matrixes (83,72 % and 94,08 %).
Assessing soil mineralogy and weathering degree by a multi-range sensor synergistic approach: From parent rock to topsoil
2022, Journal of South American Earth Sciences
Citation Excerpt :
The reduced grain size in Cr horizon justifies its lower reflectance intensity (Table 2). There was an increase in porosity due to the decrease in grain size resulting in the formation of photon traps, which enabled more photons to pass between the grains and reach deeper layers, reducing the surface scattering and the reflectance intensity (Salisbury and Eastes, 1985). The less weathered RCr1, RCr2 and RCr3 horizons of P2 showed greater content of clay minerals leading to increasing reflectance.
The weathering degree (WD) of soils is usually based on the presence of weatherable primary minerals and the content of chemical elements along the soil profile, which traditionally demand expensive, time-consuming and environmentally unfriendly laboratory analyses (e.g. wet chemistry). To address this issue, proximal sensors often operating in only a single spectral range have been used in soil WD assessments. In this sense, we analyzed in detail the performance of proximal sensors operating at several spectral ranges to assess the soil mineralogy and WD. We selected three soil profiles developed from sedimentary rocks and analyzed samples from parent rock to topsoil. Samples from each horizon of the soil profiles and from the parent rock were collected and submitted to chemical, physical and mineralogical laboratory analyses. These samples were also analyzed using several sensors operating within different spectral ranges, i.e., gamma-ray, X-ray fluorescence (XRF), visible (VIS), infrared (IR) (near infrared, NIR, shortwave infrared, SWIR, and middle infrared, Mid-IR), and the AISA Fenix hyperspectral imaging systems (VIS-NIR-SWIR). Several WD indices were calculated using laboratory (reference) and multi-range sensor data for comparison. The spectral differences in the VIS-IR range were evaluated according to the soil mineralogy and WD. Gamma-ray and XRF sensors presented high correlation with the main soil elements in oxide minerals used in the WD quantification (R ranging from 0.70 to 0.99). We detected differences associated with the WD of each horizon of all profiles using data from VIS to Mid-IR ranges. The application of linear models to AISA images enabled the mapping and quantification of main elements along one soil profile (R² ranging from 0.48 to 0.8) using a pixel-to-pixel approach, which facilitated the interpretation of the WD. Due to the specificity of each spectral region, the use of several sensors at different wavelengths provided a holistic overview of mineralogy and WD of the analyzed soil profiles, using a quick and non-destructive approach. This technique produces overlapping and/or complementary information of WD and mineralogy from all spectral ranges and can significantly assist traditional laboratory methods.
Soil weathering behavior assessed by combined spectral ranges: Insights into aggregate analysis
2021, Geoderma
Most research applying spectral sensors has focused on soil attributes quantification, with strong statistics and hundreds of data. Nevertheless, the explanation of the fundamental relationship between several spectral ranges and specific soil processes remains unclear. Soil sensing can be performed in many spectral ranges, however, the focus is usually on a specific one, which limits the knowledge of the phenomenon. Thus, this work investigated the synergistic performance of several spectral ranges (Gamma-ray; X-ray fluorescence, XRF; visible, Vis; near infrared, NIR; shortwave infrared, SWIR; and mid-infrared, MIR) on the weathering process of soils developed from magmatic material. Two soil profiles from Southeast Brazil (São Paulo state) were analyzed, assessing their chemical, physical and mineralogical data. The same profiles were analyzed by the indicated spectral ranges, which were related to mineralogy, weathering indices and mass balance (MB). The elements provided by Gamma-ray were related to the soil mineralogy and weathering degree of horizons. The XRF data enabled the calculation of MB and weathering indices, which were similar to those calculated by the traditional analysis. The spectra of the Vis-NIR-SWIR-MIR region presented alterations in their behavior related to the weathering degree of horizons. This synergic approach separated soil horizons by weathering degree more efficiently than a single spectral region. Single spectral measurements identified several elements and indices, which explained the weathering process. These results support further understanding of large datasets and their validation in statistical modeling.

View all citing articles on Scopus

View full text

NoteThe effect of particle size and porosity on spectral contrast in the mid-infrared

Abstract

Remote Sens. Environ.

Infrared emissivities of silicates: Experimental results and a cloudy atmosphere model of spectral emission from condensed particulate mediums

J. Geophys. Res.

Diffuse reflectance using infrared dispersive spectrophotometers

Appl. Spectrosc.

Bidirectional reflectance spectroscopy. 1. Theory

J. Geophys. Res.

Infrared reflectance spectra of igneous rocks, tuffs, and red sandstone from 0.5 to 22 μm

J. Opt. Soc. Amer.

Mid-Infrared Spectral Behavior of Igneous Rocks

Mid-Infrared Spectral Behavior of Sedimentary Rocks

Note
The effect of particle size and porosity on spectral contrast in the mid-infrared