Abstract
This study examines linear spectral unmixing technique for mapping the surface soil types using field spectroscopy data as the reference spectra. The investigated area is located in North Sinai, Egypt. The study employed data from the Landsat 7 ETM+ satellite sensor with improved spatial and spectral resolution. Mixed remotely sensed image pixels may lead to inaccurate classification results in most conventional image classification algorithms. Spectral unmixing may solve this problem by resolving those into separate components. Four soil type end-members were identified with minimum noise fraction and pixel purity index analyses. The identified soil types are calcareous soils, dry sabkhas, wet sabkhas, and sand dunes. Soil end-member reference spectra were collected in the field using an ASD FieldSpec Pro spectrometer. Constrained sum-to-one and non-negativity linear spectral unmixing model was applied and the soil types map was produced. The results showed that linear spectral unmixing model can be a useful tool for mapping soil types from ETM+ images.
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References
Adams JB, Smith MO, Johnson PE (1986) Spectral mixture modeling: a new analysis of rock and soil types at the Viking Landser 1 site. J Geophys Res 91:8098–8112
Afify AA, El Tapey HMA, Massoud EE (2005) The genesis and spatial distribution of soil nutrients in the physiographic units North Sinai, Egypt. J Appl Sci 20(4):748–767
ASD (2007) FieldSpec®3 user manual. ASD Inc., USA
Ayyad MA, Ghabour SI (1986) Hot deserts of Egypt and the Sudan. In: Evenari M et al (eds) Ecosystems of the world, 12B. Hot deserts and arid shrublands. Elsevier, Amsterdam, pp 149–202
Bandyopadhyay P (2007) Soil analysis, 286 pp. ISBN-13 9788189729691, 978-8189729691 (hardcover)
Baumgardner MF, Silva LF, Biehl LL, Stoner ER (1985) Reflectance properties of soil. Adv Agron 38:1–44
Ben-Dor E, Banin A (1994) Visible and near infrared (0.4–1.1 mm) analysis of arid and semiarid soils. Remote Sens Environ 48:261–274
Boardman J (1994) Geometric mixture analysis of imaging spectrometry data. In: International Geoscience and Remote Sensing Symposium, IGARSS’94, Surface and Atmospheric Remote Sensing: Technologies, Data analysis and Interpretation, vol IV. California, USA, pp 2369–2371
Boardman JW, Kruse FA (1994). Automated spectral analysis: a geologic example using AVIRIS data, north Grapevine Mountains, Nevada. Proceedings of the Tenth Thematic Conference on Geologic Remote Sensing, Environmental Research Institute of Michigan, Ann Arbor, MI, pp I-407–I-418
Boardman JW, Kruse FA, Green RO (1995) Mapping target signatures via partial unmixing of AVIRIS data. In: Summaries of the fifth JPL Airborne Earth Science Workshop, JPL Publication 95-1, Jet Probulsion Laboratory, CA, USA, pp 23–23
Chander G, Markham B, Helder D (2009) Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote Sens Environ 113:893–903
Clark RN (1999) Spectroscopy of rocks and minerals, and principals of spectroscopy. Wiley, New York, pp 3–58
FAO (2006) Guidelines for soil profile description. Soil Resources, Management and Conservation Service, Land and Water Development Division, FAO, Rome
Green AA, Beramn M, Switzer P, Craig MD (1988) A transformation for ordering multispectral data in terms of image quality and implications for noise removal. IEEE Trans Geosc Rem Sens 26(1):65–74
Hassan MAA (2002) Environmental studies on coastal zone soils of the North Sinai Peninsula (Egypt) using remote sensing techniques. PhD thesis, Bundesforschungsanstalt für Landwirtschaft (FAL), Braunschweig, Germany
Hola NSS (2000) Landuse mapping for selected areas of El-Salam Canal Command-Sinai, Using Remote Sensing and GIS. MSc, Faculty of Agricultural, Cairo University, Cairo Egypt
Irons JR, Weismiller RA, Petersen GW (1989) Soil reflectance. Theory and applications of optical remote sensing. Wiley, New York, pp 66–106
ITT (2008) ITT corporation ENVI 4.6 software, 1133 Westchester Avenue, White Plains, NY 10604, USA
Lillesand TM, Kiefer RW (2007) Remote sensing and image interpretation, 5th edn. Wiley, New York, p 820
Lu D, Morana E, Batistella M (2003) Linear mixture model applied to Amazonian vegetation classification. Remote Sens Environ 87:456–469
Lunetta RS (1998) Applications, project formulation, and analytical approach. In: Lunetta RS, Elvidge CD (eds) Remote sensing change detection: environmental monitoring methods and applications. Taylor & Francis, London, pp 1–19
Rashed T, Weeks JR, Roberts D, Rogan J, Powell R (2003) Measuring the physical composition of urban morphology using multiple endmember spectral mixture models. Photogramm Eng Remote Sens 69:1011–1020
Roberts DA, Smith MO, Adams JB (1993) Green vegetation, nonphotosynthetic vegetation and soils in AVIRIS data. Remote Sens Environ 44:255–269
Roberts DA, Batista GT, Pereira JLG, Waller EK, Nelson B (1998) Change identification using multitemporal spectral mixture analysis: applications in eastern Amazonia. In: Lunetta RS, Elvidge CD (eds) Remote sensing change detection: environmental monitoring methods and applications. Taylor & Francis, London, pp 137–161
Said R (1990) The geology of Egypt. A.A. Balkema, USA, p 437
Smith MO, Ustin SL, Adams JB, Gillespie AR (1990) Vegetation in deserts: I. A regional measure of abundance from multi-spectral images. Remote Sens Environ 31:1–26
Soil Survey Staff (1951) Soil survey manual. U.S. Department of Agriculture Handbook No. 18, Government Printing Office, Washington, DC
Theseira MA, Thomas G, Taylor JC, Gemmell F, Varjo J (2003) Sensitivity of mixture modeling to end-member selection. Int J Remote Sens 24(7):1559–1575
Tompkins S, Mustard JF, Pieters CM, Forsyth DW (1997) Optimization of endmembers for spectral mixture analysis. Remote Sens Environ 59:472–489
USDA (2010) Keys to soil taxonomy, 11th edn. United States Department of Agriculture, USA
Weng Q, Lu D, Schubring J (2004) Estimation of land surface temperature-vegetation abundance relationship for urban heat island studies. Remote Sens Environ 89:467–483
Wu C, Murray A (2003) Estimating impervious surface distribution by spectral mixture analysis. Remote Sens Environ 84:493–505
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The authors acknowledge the National Authority for Remote Sensing and Space Sciences (NARSS) for funding an internal research project principally investigated by the third author of the present work, from which data of the present work were derived.
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Saleh, A.M., Belal, A.B. & Arafat, S.M. Identification and mapping of some soil types using field spectrometry and spectral mixture analyses: a case study of North Sinai, Egypt. Arab J Geosci 6, 1799–1806 (2013). https://doi.org/10.1007/s12517-011-0501-6
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DOI: https://doi.org/10.1007/s12517-011-0501-6