Abstract

Quartz is the most abundant mineral on the earth's surface. It is spectrally active in the longwave infrared (LWIR) region with no significant spectral features in the optical domain, i.e., visible–near-infrared–shortwave-infrared (Vis–NIR–SWIR) region. Several space agencies are planning to mount optical image spectrometers in space, with one of their missions being to map raw materials. However, these sensors are active across the optical region, making the spectral identification of quartz mineral problematic. This study demonstrates that indirect relationships between the optical and LWIR regions (where quartz is spectrally dominant) can be used to assess quartz content spectrally using solely the optical region. To achieve this, we made use of the legacy Israeli soil spectral library, which characterizes arid and semiarid soils through comprehensive chemical and mineral analyses along with spectral measurements across the Vis–NIR–SWIR region (reflectance) and LWIR region (emissivity). Recently, a Soil Quartz Clay Mineral Index (SQCMI) was developed using mineral-related emissivity features to determine the content of quartz, relative to clay minerals, in the soil. The SQCMI was highly and significantly correlated with the Vis–NIR–SWIR spectral region (R2 = 0.82, root mean square error (RMSE) = 0.01, ratio of performance to deviation (RPD) = 2.34), whereas direct estimation of the quartz content using a gradient-boosting algorithm against the Vis–NIR–SWIR region provided poor results (R2 = 0.45, RMSE = 15.63, RPD = 1.32). Moreover, estimation of the SQCMI value was even more accurate when only the 2000–2450 nm spectral range (atmospheric window) was used (R2 = 0.9, RMSE = 0.005, RPD = 1.95). These results suggest that reflectance data across the 2000–2450 nm spectral region can be used to estimate quartz content, relative to clay minerals in the soil satisfactorily using hyperspectral remote sensing means.

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2020 (1)

G. Notesco, S. Weksler, and E Ben-Dor. “Application of Hyperspectral Remote Sensing in the Longwave Infrared Region to Assess the Influence of Dust from the Desert on Soil Surface Mineralogy”. Remote Sens. 2020; 12(9): 1388.

2019 (3)

Y. Ogen, J. Zaluda, N. Francos, et al. “Cluster-Based Spectral Models for a Robust Assessment of Soil Properties”. Geoderma. 2019; 340: 175–184.

Y. Ogen, S. Faigenbaum-Golovin, A. Granot, et al. “Removing Moisture Effect on Soil Reflectance Properties: A Case Study of Clay Content Prediction”. Pedosphere. 2019; 29(4): 421–431.

G. Notesco, S. Weksler, and E Ben-Dor. “Mineral Classification of Soils Using Hyperspectral Longwave Infrared (LWIR) Ground-Based Data”. Remote Sens. 2019; 11(12): 1429.

2017 (2)

I. Entezari, B. Rivard, M. Geramian, et al. “Predicting the Abundance of Clays and Quartz in Oil Sands Using Hyperspectral Measurements”. Int. J. Appl. Earth Obs. Geoinf. 2017; 59: 1–8.

L. Liu, M. Ji, and M Buchroithner. “Combining Partial Least Squares and the Gradient-Boosting Method for Soil Property Retrieval Using Visible Near-Infrared Shortwave Infrared Spectra”. Remote Sens. 2017; 9(12): 1299.

2015 (1)

E. Ben-Dor, C. Ong, I.C Lau. “Reflectance Measurements of Soils in the Laboratory: Standards and Protocols”. Geoderma. 2015; 245–246: 112–124.

2014 (3)

J.M. Soriano-Disla, L.J. Janik, R.A. Viscarra Rossel, et al. The Performance of Visible, Near-, and Mid-Infrared Reflectance Spectroscopy for Prediction of Soil Physical, Chemical, and Biological Properties”. Appl. Spectrosc. Rev. 2014; 49(2): 139–186.

G. Notesco, V. Kopačková, and P. Rojík, et al. “Mineral Classification of Land Surface Using Multispectral LWIR and Hyperspectral SWIR Remote-Sensing Data. A Case Study over the Sokolov Lignite Open-Pit Mines, the Czech Republic”. Remote Sens. 2014; 6(8): 7005–7025.

M.C. Sarathjith, B.S. Das, S.P. Wani, et al. “Dependency Measures for Assessing the Covariation of Spectrally Active and Inactive Soil Properties in Diffuse Reflectance Spectroscopy”. Soil Sci. Soc. Am. J. 2014; 78(5): 1522–1530.

2013 (2)

D. Cozzolino, W.U. Cynkar, R.G. Dambergs, et al. “In Situ Measurement of Soil Chemical Composition by Near-Infrared Spectroscopy: A Tool Toward Sustainable Vineyard Management”. Commun. Soil Sci. Plant Anal. 2013; 44(10): 1610–1619.

S. Adar, Y. Shkolnisky, G. Notesco, et al. “Using Visible Spectral Information to Predict Long-Wave Infrared Spectral Emissivity: A Case Study over the Sokolov Area of the Czech Republic with an Airborne Hyperspectral Scanner Sensor”. Remote Sens. 2013; 5(11): 5757–5782.

2011 (1)

F. Pedregosa, G. Varoquaux, A. Gramfort, et al. “Scikit-Learn: Machine Learning in Python”. J. Mach. Learning Res. 2011; 12: 2825–2830.

2010 (1)

R.A. Viscarra Rossel and T Behrens. Using Data Mining to Model and Interpret Soil Diffuse Reflectance Spectra”. Geoderma. 2010; 158(1–2): 46–54.

2004 (1)

M.L. Whiting, L. Li, S.L Ustin. “Predicting Water Content Using Gaussian Model on Soil Spectra”. Remote Sens. Environ. 2004; 89(4): 535–552.

2002 (1)

E Ben-Dor. “Quantitative Remote Sensing of Soil Properties”. Adv. Agron. 2002; 75: 173–243.

2001 (2)

J.H Friedman. “Greedy Function Approximation: A Gradient Boosting Machine”. Ann. Statistics. 2001; 29(5): 1189–1232.

C.-W. Chang, D.A. Laird, M.J. Mausbach, et al. “Near-Infrared Reflectance Spectroscopy–Principal Components Regression Analyses of Soil Properties”. Soil Sci. Soc. Am. J. 2001; 65(2): 480–490.

1995 (2)

F.A. Madsen, M.C. Rose, and R Cee. “Review of Quartz Analytical Methodologies: Present and Future Needs”. Appl. Occup. Environ. Hyg. 1995; 10(12): 991–1002.

A. Singer and V Berkgaut. “Cation Exchange Properties of Hydrothermally Treated Coal Fly Ash”. Environ. Sci. Technol. 1995; 29(7): 1748–1753.

1990 (1)

T.H. Demetriades-Shah, M.D. Steven, J.A Clark. “High Resolution Derivative Spectra in Remote Sensing”. Remote Sens. Environ. 1990; 33(1): 55–64.

1974 (1)

M. Gal, A.J. Amiel, and S Ravikovitch. “Clay Mineral Distribution and Origin in the Soil Types of Israel”. J. Soil Sci. 1974; 25(1): 79–89.

1970 (1)

A. Banin and A Amiel. “A Correlative Study of the Chemical and Physical Properties of a Group of Natural Soils of Israel”. Geoderma. 1970; 3(3): 185–198.

1964 (1)

A. Savitzky, M.J.E Golay. “Smoothing and Differentiation of Data by Simplified Least Squares Procedures”. Anal. Chem. 1964; 36(8): 1627–1639.

Adar, S.

S. Adar, Y. Shkolnisky, G. Notesco, et al. “Using Visible Spectral Information to Predict Long-Wave Infrared Spectral Emissivity: A Case Study over the Sokolov Area of the Czech Republic with an Airborne Hyperspectral Scanner Sensor”. Remote Sens. 2013; 5(11): 5757–5782.

Amiel, A

A. Banin and A Amiel. “A Correlative Study of the Chemical and Physical Properties of a Group of Natural Soils of Israel”. Geoderma. 1970; 3(3): 185–198.

Amiel, A.J.

M. Gal, A.J. Amiel, and S Ravikovitch. “Clay Mineral Distribution and Origin in the Soil Types of Israel”. J. Soil Sci. 1974; 25(1): 79–89.

Banin, A.

A. Banin and A Amiel. “A Correlative Study of the Chemical and Physical Properties of a Group of Natural Soils of Israel”. Geoderma. 1970; 3(3): 185–198.

Behrens, T

R.A. Viscarra Rossel and T Behrens. Using Data Mining to Model and Interpret Soil Diffuse Reflectance Spectra”. Geoderma. 2010; 158(1–2): 46–54.

Ben-Dor, E

G. Notesco, S. Weksler, and E Ben-Dor. “Application of Hyperspectral Remote Sensing in the Longwave Infrared Region to Assess the Influence of Dust from the Desert on Soil Surface Mineralogy”. Remote Sens. 2020; 12(9): 1388.

G. Notesco, S. Weksler, and E Ben-Dor. “Mineral Classification of Soils Using Hyperspectral Longwave Infrared (LWIR) Ground-Based Data”. Remote Sens. 2019; 11(12): 1429.

E Ben-Dor. “Quantitative Remote Sensing of Soil Properties”. Adv. Agron. 2002; 75: 173–243.

Ben-Dor, E.

E. Ben-Dor, C. Ong, I.C Lau. “Reflectance Measurements of Soils in the Laboratory: Standards and Protocols”. Geoderma. 2015; 245–246: 112–124.

Berkgaut, V

A. Singer and V Berkgaut. “Cation Exchange Properties of Hydrothermally Treated Coal Fly Ash”. Environ. Sci. Technol. 1995; 29(7): 1748–1753.

Buchroithner, M

L. Liu, M. Ji, and M Buchroithner. “Combining Partial Least Squares and the Gradient-Boosting Method for Soil Property Retrieval Using Visible Near-Infrared Shortwave Infrared Spectra”. Remote Sens. 2017; 9(12): 1299.

Cee, R

F.A. Madsen, M.C. Rose, and R Cee. “Review of Quartz Analytical Methodologies: Present and Future Needs”. Appl. Occup. Environ. Hyg. 1995; 10(12): 991–1002.

Chang, C.-W.

C.-W. Chang, D.A. Laird, M.J. Mausbach, et al. “Near-Infrared Reflectance Spectroscopy–Principal Components Regression Analyses of Soil Properties”. Soil Sci. Soc. Am. J. 2001; 65(2): 480–490.

Clark, J.A

T.H. Demetriades-Shah, M.D. Steven, J.A Clark. “High Resolution Derivative Spectra in Remote Sensing”. Remote Sens. Environ. 1990; 33(1): 55–64.

Cozzolino, D.

D. Cozzolino, W.U. Cynkar, R.G. Dambergs, et al. “In Situ Measurement of Soil Chemical Composition by Near-Infrared Spectroscopy: A Tool Toward Sustainable Vineyard Management”. Commun. Soil Sci. Plant Anal. 2013; 44(10): 1610–1619.

Cynkar, W.U.

D. Cozzolino, W.U. Cynkar, R.G. Dambergs, et al. “In Situ Measurement of Soil Chemical Composition by Near-Infrared Spectroscopy: A Tool Toward Sustainable Vineyard Management”. Commun. Soil Sci. Plant Anal. 2013; 44(10): 1610–1619.

Dambergs, R.G.

D. Cozzolino, W.U. Cynkar, R.G. Dambergs, et al. “In Situ Measurement of Soil Chemical Composition by Near-Infrared Spectroscopy: A Tool Toward Sustainable Vineyard Management”. Commun. Soil Sci. Plant Anal. 2013; 44(10): 1610–1619.

Das, B.S.

M.C. Sarathjith, B.S. Das, S.P. Wani, et al. “Dependency Measures for Assessing the Covariation of Spectrally Active and Inactive Soil Properties in Diffuse Reflectance Spectroscopy”. Soil Sci. Soc. Am. J. 2014; 78(5): 1522–1530.

Demetriades-Shah, T.H.

T.H. Demetriades-Shah, M.D. Steven, J.A Clark. “High Resolution Derivative Spectra in Remote Sensing”. Remote Sens. Environ. 1990; 33(1): 55–64.

Entezari, I.

I. Entezari, B. Rivard, M. Geramian, et al. “Predicting the Abundance of Clays and Quartz in Oil Sands Using Hyperspectral Measurements”. Int. J. Appl. Earth Obs. Geoinf. 2017; 59: 1–8.

Faigenbaum-Golovin, S.

Y. Ogen, S. Faigenbaum-Golovin, A. Granot, et al. “Removing Moisture Effect on Soil Reflectance Properties: A Case Study of Clay Content Prediction”. Pedosphere. 2019; 29(4): 421–431.

Francos, N.

Y. Ogen, J. Zaluda, N. Francos, et al. “Cluster-Based Spectral Models for a Robust Assessment of Soil Properties”. Geoderma. 2019; 340: 175–184.

Friedman, J.H

J.H Friedman. “Greedy Function Approximation: A Gradient Boosting Machine”. Ann. Statistics. 2001; 29(5): 1189–1232.

Gal, M.

M. Gal, A.J. Amiel, and S Ravikovitch. “Clay Mineral Distribution and Origin in the Soil Types of Israel”. J. Soil Sci. 1974; 25(1): 79–89.

Geramian, M.

I. Entezari, B. Rivard, M. Geramian, et al. “Predicting the Abundance of Clays and Quartz in Oil Sands Using Hyperspectral Measurements”. Int. J. Appl. Earth Obs. Geoinf. 2017; 59: 1–8.

Golay, M.J.E

A. Savitzky, M.J.E Golay. “Smoothing and Differentiation of Data by Simplified Least Squares Procedures”. Anal. Chem. 1964; 36(8): 1627–1639.

Gramfort, A.

F. Pedregosa, G. Varoquaux, A. Gramfort, et al. “Scikit-Learn: Machine Learning in Python”. J. Mach. Learning Res. 2011; 12: 2825–2830.

Granot, A.

Y. Ogen, S. Faigenbaum-Golovin, A. Granot, et al. “Removing Moisture Effect on Soil Reflectance Properties: A Case Study of Clay Content Prediction”. Pedosphere. 2019; 29(4): 421–431.

Janik, L.J.

J.M. Soriano-Disla, L.J. Janik, R.A. Viscarra Rossel, et al. The Performance of Visible, Near-, and Mid-Infrared Reflectance Spectroscopy for Prediction of Soil Physical, Chemical, and Biological Properties”. Appl. Spectrosc. Rev. 2014; 49(2): 139–186.

Ji, M.

L. Liu, M. Ji, and M Buchroithner. “Combining Partial Least Squares and the Gradient-Boosting Method for Soil Property Retrieval Using Visible Near-Infrared Shortwave Infrared Spectra”. Remote Sens. 2017; 9(12): 1299.

Kopacková, V.

G. Notesco, V. Kopačková, and P. Rojík, et al. “Mineral Classification of Land Surface Using Multispectral LWIR and Hyperspectral SWIR Remote-Sensing Data. A Case Study over the Sokolov Lignite Open-Pit Mines, the Czech Republic”. Remote Sens. 2014; 6(8): 7005–7025.

Laird, D.A.

C.-W. Chang, D.A. Laird, M.J. Mausbach, et al. “Near-Infrared Reflectance Spectroscopy–Principal Components Regression Analyses of Soil Properties”. Soil Sci. Soc. Am. J. 2001; 65(2): 480–490.

Lau, I.C

E. Ben-Dor, C. Ong, I.C Lau. “Reflectance Measurements of Soils in the Laboratory: Standards and Protocols”. Geoderma. 2015; 245–246: 112–124.

Li, L.

M.L. Whiting, L. Li, S.L Ustin. “Predicting Water Content Using Gaussian Model on Soil Spectra”. Remote Sens. Environ. 2004; 89(4): 535–552.

Liu, L.

L. Liu, M. Ji, and M Buchroithner. “Combining Partial Least Squares and the Gradient-Boosting Method for Soil Property Retrieval Using Visible Near-Infrared Shortwave Infrared Spectra”. Remote Sens. 2017; 9(12): 1299.

Madsen, F.A.

F.A. Madsen, M.C. Rose, and R Cee. “Review of Quartz Analytical Methodologies: Present and Future Needs”. Appl. Occup. Environ. Hyg. 1995; 10(12): 991–1002.

Mausbach, M.J.

C.-W. Chang, D.A. Laird, M.J. Mausbach, et al. “Near-Infrared Reflectance Spectroscopy–Principal Components Regression Analyses of Soil Properties”. Soil Sci. Soc. Am. J. 2001; 65(2): 480–490.

Notesco, G.

G. Notesco, S. Weksler, and E Ben-Dor. “Application of Hyperspectral Remote Sensing in the Longwave Infrared Region to Assess the Influence of Dust from the Desert on Soil Surface Mineralogy”. Remote Sens. 2020; 12(9): 1388.

G. Notesco, S. Weksler, and E Ben-Dor. “Mineral Classification of Soils Using Hyperspectral Longwave Infrared (LWIR) Ground-Based Data”. Remote Sens. 2019; 11(12): 1429.

G. Notesco, V. Kopačková, and P. Rojík, et al. “Mineral Classification of Land Surface Using Multispectral LWIR and Hyperspectral SWIR Remote-Sensing Data. A Case Study over the Sokolov Lignite Open-Pit Mines, the Czech Republic”. Remote Sens. 2014; 6(8): 7005–7025.

S. Adar, Y. Shkolnisky, G. Notesco, et al. “Using Visible Spectral Information to Predict Long-Wave Infrared Spectral Emissivity: A Case Study over the Sokolov Area of the Czech Republic with an Airborne Hyperspectral Scanner Sensor”. Remote Sens. 2013; 5(11): 5757–5782.

Ogen, Y.

Y. Ogen, J. Zaluda, N. Francos, et al. “Cluster-Based Spectral Models for a Robust Assessment of Soil Properties”. Geoderma. 2019; 340: 175–184.

Y. Ogen, S. Faigenbaum-Golovin, A. Granot, et al. “Removing Moisture Effect on Soil Reflectance Properties: A Case Study of Clay Content Prediction”. Pedosphere. 2019; 29(4): 421–431.

Ong, C.

E. Ben-Dor, C. Ong, I.C Lau. “Reflectance Measurements of Soils in the Laboratory: Standards and Protocols”. Geoderma. 2015; 245–246: 112–124.

Pedregosa, F.

F. Pedregosa, G. Varoquaux, A. Gramfort, et al. “Scikit-Learn: Machine Learning in Python”. J. Mach. Learning Res. 2011; 12: 2825–2830.

Ravikovitch, S

M. Gal, A.J. Amiel, and S Ravikovitch. “Clay Mineral Distribution and Origin in the Soil Types of Israel”. J. Soil Sci. 1974; 25(1): 79–89.

Rivard, B.

I. Entezari, B. Rivard, M. Geramian, et al. “Predicting the Abundance of Clays and Quartz in Oil Sands Using Hyperspectral Measurements”. Int. J. Appl. Earth Obs. Geoinf. 2017; 59: 1–8.

Rojík, P.

G. Notesco, V. Kopačková, and P. Rojík, et al. “Mineral Classification of Land Surface Using Multispectral LWIR and Hyperspectral SWIR Remote-Sensing Data. A Case Study over the Sokolov Lignite Open-Pit Mines, the Czech Republic”. Remote Sens. 2014; 6(8): 7005–7025.

Rose, M.C.

F.A. Madsen, M.C. Rose, and R Cee. “Review of Quartz Analytical Methodologies: Present and Future Needs”. Appl. Occup. Environ. Hyg. 1995; 10(12): 991–1002.

Sarathjith, M.C.

M.C. Sarathjith, B.S. Das, S.P. Wani, et al. “Dependency Measures for Assessing the Covariation of Spectrally Active and Inactive Soil Properties in Diffuse Reflectance Spectroscopy”. Soil Sci. Soc. Am. J. 2014; 78(5): 1522–1530.

Savitzky, A.

A. Savitzky, M.J.E Golay. “Smoothing and Differentiation of Data by Simplified Least Squares Procedures”. Anal. Chem. 1964; 36(8): 1627–1639.

Shkolnisky, Y.

S. Adar, Y. Shkolnisky, G. Notesco, et al. “Using Visible Spectral Information to Predict Long-Wave Infrared Spectral Emissivity: A Case Study over the Sokolov Area of the Czech Republic with an Airborne Hyperspectral Scanner Sensor”. Remote Sens. 2013; 5(11): 5757–5782.

Singer, A.

A. Singer and V Berkgaut. “Cation Exchange Properties of Hydrothermally Treated Coal Fly Ash”. Environ. Sci. Technol. 1995; 29(7): 1748–1753.

Soriano-Disla, J.M.

J.M. Soriano-Disla, L.J. Janik, R.A. Viscarra Rossel, et al. The Performance of Visible, Near-, and Mid-Infrared Reflectance Spectroscopy for Prediction of Soil Physical, Chemical, and Biological Properties”. Appl. Spectrosc. Rev. 2014; 49(2): 139–186.

Steven, M.D.

T.H. Demetriades-Shah, M.D. Steven, J.A Clark. “High Resolution Derivative Spectra in Remote Sensing”. Remote Sens. Environ. 1990; 33(1): 55–64.

Ustin, S.L

M.L. Whiting, L. Li, S.L Ustin. “Predicting Water Content Using Gaussian Model on Soil Spectra”. Remote Sens. Environ. 2004; 89(4): 535–552.

Varoquaux, G.

F. Pedregosa, G. Varoquaux, A. Gramfort, et al. “Scikit-Learn: Machine Learning in Python”. J. Mach. Learning Res. 2011; 12: 2825–2830.

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Supplementary Material (1)

NameDescription
Supplement 1       sj-pdf-1-asp-10.1177_0003702821998302 - Supplemental material for Estimation of the Relative Abundance of Quartz to Clay Minerals Using the Visible–Near-Infrared–Shortwave-Infrared Spectral Region

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