Abstract
Hyperspectral images provide rich spectral and spatially continuous information that can be used for soil mineralogy discrimination. This paper proposes a method to evaluate the feasibility of Hyperion image in the rapid prediction of soil mineralogy. Four areas in Egypt were chosen for the current study. Preprocessing of the Hyperion data was done before applying the atmospheric correction. The minimum noise fraction transformation was used to segregate noise in the data. Various techniques were applied to the studied areas in which mixture tune matched filtering gave good results in a prediction of the end-members. Then, it employed to predict soil minerals in each cell using a spectral unmixing method. Illite, chlorite, calcite, dolomite, kaolinite, smectite, quartz, hematite, goethite, vermiculite, palygorskite and some feldspar were identified. In addition, sand and limestone, calcite and dolomite, and sand surface from similarly bright clouds can be distinguished easily based on the proposed method. The soil minerals obtained from X-ray diffraction analysis of the soil samples are in conformity with spectrally dominant mineralogy from Hyperion data. Different minerals can be identified using this method without any knowledge of field spectra or any a priori field data, thus configuring a “true” remote sensing method.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
E. Ben-Dor, S. Chabrillat, J.A.M. Demattê, G.R. Taylor, J. Hill, M.L. Whiting, and S. Sommer, “Using imaging spectroscopy to study soil properties,” Remote Sens. Environ. 113, S38–S55 (2009).
E. Ben-Dor, K. Patkin, A. Banin, and A. Karnieli, “Mapping of several soil properties using Dais-7915 hyperspectral scanner data—a case study over clayey soil in Israel,” Int. J. Remote Sens. Appl. 23, 20 (2002).
D. L. Bish, and M. Plötze, “X-ray powder diffraction with emphasis on qualitative and quantitative analysis in industrial mineralogy,” in {EMU Notes in Mineralogy, Vol. 9: Industrial Minerals}, Ed. by G. Christidis (Word-Press, Jena, 2011), pp. 35–76.
J. W. Boardman, “Precision geocoding of Aviris lowaltitude data: lessons learned in 1998,” Proceedings of the 8th JPL Airborne Earth Science Workshop (Jet Propulsion Lab., Pasadena, CA, 1999), pp. 63–68.
J. W. Boardman, “Automated spectral unmixing of Aviris data using convex geometry concepts,” Summaries of the Fourth JPL Airborne Geoscience Workshop (NASA Jet Propulsion Lab., Pasadena, CA, 1993), pp. 11–14.
J. W. Boardman, and F. A. Kruse, “Automated spectral analysis: a geological example using Aviris data, North Grapevine Mountains, Nevada,” Proceedings ERIM Tenth Thematic Conf. on Geologic Remote Sensing (Ann Arbor, MI, 1994), pp. 407–418.
S. Chabrillat, A. F. H. Goetz, L. Krosley, and H. W. Olsen, “Use of hyperspectral images in the identification and mapping of expansive clay soils and the role of spatial resolution,” Remote Sens. Environ. 82, 431–445 (2002).
D. Cozzolino, and A. Moron, “Potential of near-infrared reflectance spectroscopy and chemometrics to predict soil organic carbon fractions,” Soil Tillage Res. 85, 78–85 (2006).
O. Crouvi, E. Ben-Dor, M. Beyth, D. Avigad, and R. Amit, “Quantitative mapping of arid alluvial fan surfaces using field spectrometer and hyperspectral remote sensing,” Remote Sens. Environ. 104, 103–117 (2006).
J. A. M. Dematte, M. V. Galdos, R. V. Guimaraes, A. M. Genu, M. R. Nanni, and J. Zullo, “Quantification of tropical soil attributes from Etm+/Landsat-7 data,” Int. J. Remote Sens. 28, 3813–3829 (2007).
M. Egli, M. Nater, A. Mirabella, S. Raimondi, M. Plötze, and L. Alioth, “Clay minerals, oxyhydroxide formation, element leaching, and humus development in volcanic soils,” Geoderma 143, 101–114 (2008).
N. S. Embabi, The Geomorphology of Egypt. Landform and Evolution: The Nile Valley and Western Desert (Egyptian Geographical Society, Cairo, 2004), Vol.1.
L. S. Galvão, A. R. Formaggio, E. D. Couto, and D. A. Roberts, “Relationships between the mineralogical and chemical composition of tropical soils and topography from hyperspectral remote sensing data,” ISPRS J. Photogramm. Remote Sens. 63, 259–271 (2008).
Y. Ge, J. A. Thomasson, and R. Sui, “Remote sensing of soil properties in precision agriculture: a review,” ASABE Annual International Meeting (Portland, OR, 2006), No. 061176.
C. Gomez, P. Lagacherie, and G. Coulouma, “Continuum removal versus Plsr method for clay and calcium carbonate content estimation from laboratory and airborne hyperspectral measurements,” Geoderma 148, 141–148 (2008).
R. B. Gomez, “The power of hyperspectral technology,” Proceedings of the 2nd International Conf. on Earth Observation and Environmental Information (Cairo, 2000).
A. A. Green, and M. D. Craig, “Analysis of aircraft spectrometer data with logarithmic residuals,” Proceedings AIS Workshop, April 8–10, 1985 (Jet Propulsion Lab., Pasadena, CA, 1985), pp. 111–119.
S. Y. Hong, B. Minasny, K. H. Han, Y. Kim, and K. Lee, “Predicting and mapping soil available water capacity in Korea,” Peer J 1, p. e71 (2013).
J. R. Jenson, Introductory Digital Image Processing. A Remote Sensing Perspective (Prentice Hall, Upper Saddle River, 2005).
R. F. Kokaly, “Investigating a physical basis for spectroscopic estimates of leaf nitrogen concentration,” Remote Sens. Environ. 75, 153–161 (2001).
F. A. Kruse, J. W. Boardman, and J. F. Huntington, “Comparison of airborne hyperspectral data and Eo-1 hyperion for mineral mapping,” IEEE Trans. Geosci. Remote Sens. 41, 1388–1400 (2002).
P. Lagacherie, F. Baret, J. Feret, J. M. Netto, and J. M. Robbez-Masson, “Estimation of soil clay and calcium carbonate using laboratory, field and airborne hyperspectral measurements,” Remote Sens. Environ. 112, 825–835 (2008).
J. S. R. Madeira Netto, J. M. Robbez-Masson, and E. Martins, “Visible-NIR hyperspectral imagery for discriminating soil types in the La Peyne watershed (France),” in {Digital Soil Mapping: an Introductory Perspective}, Ed. by P. Lagacherie, A. B. McBratney, and M. Voltz (Elsevier, Amsterdam, 2006).
T. Magendran, and S. Sanjeevi, “Hyperion image analysis and linear spectral unmixing to evaluate the grades of iron ores in the part of Noamundi, Eastern India,” Int. J. Appl. Earth Obs. Geoinf. 26, 413–426 (2013).
D. F. Malley, and P. C. Williams, “Use of near-infrared reflectance spectroscopy in prediction of heavy metals in freshwater sediment by their association with organic matter,” Environ. Sci. Technol. 31, 3461–3467. (1997).
C. Mavris, M. Plötze, A. Mirabella, D. Giaccai, G. Valboa, and M. Egli, “Clay mineral evolution along a soil chronosequence in an alpine proglacial area,” Geoderma 165, 106–117 (2011).
E. H. Mohamed, PhD Thesis (Ain Shams University, Cairo, 1997).
Y. E. Molan, D. Refali, and A. H. Tarashti, “Mineral mapping in the Maherabad area, Eastern Iran, using Hymap remote sensing data,” Int. J. Appl. Earth Obs. Geoinf. 27, 117–127 (2013).
V. L. Mulder, S. de Bruin, and M. E. Schaepman, “Towards spectroscopic modeling of composite mineralogy,” 9th Swiss Geoscience Meeting (Zürich, 2011).
V. L. Mulder, S. de Bruin, M. E. Schaepman, and T. R. Mayr, “The use of remote sensing in soil and terrain mapping—a review,” Geoderma 162, 1–19 (2011).
M. R. Nanni, and J. A. M. Demattê, “Spectral reflectance methodology in comparison to traditional soil analysis,” Soil Sci. Soc. Am. J. 70, 393–407 (2006).
E. S. E. Omran, “A stochastic simulation model to early predict susceptible areas to water table level fluctuations in North Sinai, Egypt,” Egypt. J. of Remote Sens 19, 235–257 (2016). doi 10.1016/j.ejrs.2016.03.001
E. S. E. Omran, “On-the-go digital soil mapping for precision agriculture,” Int. J. Remote Sens. Appl. 2, 20–38 (2012).
A. Plaza, J. A. Benediktsson, J. Boardman, J. Brazile, L. Bruzzone, G. Camps-Valls, J. Chanussot, M. Fauvel, P. Gamba, J.A. Gualtieri, M. Marconicini, J. C. Tilton, and G. Trianni, “Recent advances in techniques for hyperspectral image processing,” Remote Sens. Environ. 113, 110–122 (2009).
S. S. Ray, J. P. Singh, G. Das, and S. Panigrahy, “Use of high resolution remote sensing data for generating site-specific soil management plan,” XX ISPRS Congress, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences (Ahmedabad, 2004), pp. 127–131.
R. Said, The Geology of Egypt (Balkema, Rotterdam, 1990).
S. S. Salaj, Prabhakaran, R. Upadhyay, and S. K. Srivastav, Mineral Abundance Mapping Using Hyperion Dataset in Udaipur, India (Indian Institute of Remote Sensing, Dehradun, 2012).
T. Selige, J. Bohner, and U. Schmidhalter, “High resolution topsoil mapping using hyperspectral image and field data in multivariate regression modeling procedures,” Geoderma 136, 235–244 (2006).
G. M. Smith, and P. J. Curran, “Methods for estimating image signal-to-noise ratio (SNR),” in, Ed. by P. Atkinson and N. Tate (Wiley, Chichester, 2000), pp. 61–74.
R. A. Viscarra Rossel, D. J. J. Walvoort, A. B. McBratney, L. J. Janik, and J. O. Skjemstad, “Visible, nearinfrared, mid-infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties,” Geoderma 131, 59–75 (2006).
M. H. Zadeh, M. H. Tangestani, F. V. Roldan, and I. Yusta, “Sub-pixel mineral mapping of a porphyry copper belt using Eo-1 Hyperion data,” Adv. Space Res. 53, 440–451 (2013).
X. Zhang, and X. Li, “Lithological mapping from hyperspectral data by improved use of spectral angle mapper,” Int. J. Appl. Earth Obs. Geoinf. 31, 95–109 (2014).
Author information
Authors and Affiliations
Corresponding author
Additional information
The article is published in the original.
Rights and permissions
About this article
Cite this article
Omran, E.S.E. Rapid prediction of soil mineralogy using imaging spectroscopy. Eurasian Soil Sc. 50, 597–612 (2017). https://doi.org/10.1134/S106422931705012X
Received:
Published:
Issue Date:
DOI: https://doi.org/10.1134/S106422931705012X