ORIGINAL PAPER Identification and mapping of some soil types using field spectrometry and spectral mixture analyses: a case study of North Sinai, Egypt A. M. Saleh & A. B. Belal & S. M. Arafat Received: 27 August 2011 /Accepted: 2 December 2011 /Published online: 22 December 2011 # Saudi Society for Geosciences 2011 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 im- proved 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 iden- tified 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. Keywords Soil mapping . Field spectrometry . Spectral mixture analyses . North Sinai Egypt Introduction Soil is one of the most valuable resources. Information from soil and land resource survey is necessary for better manage- ment and wise soil use. Soil inventory is often carried out as part of a regional planning and development process in order to determine the location and extent of various soil types and variables. The spatial and temporal variability of surface pro- cesses makes soil properties variable and, therefore, makes it difficult to measure directly from their reflectance spectra even under controlled laboratory conditions (Ben-Dor and Banin 1994). Unlike vegetation spectra, the shape of reflec- tance spectra obtained from soils are mainly invariant in the spectral regions (0.4–1.2 μm; Clark 1999). This may be due to a combined effect of different factors that can affect surface spectral reflectance of soils and make it non-consistent through the spectrum region. The reflectance from an image pixel is a mixture of the individual reflectance spectra of surface materials (Adams et al. 1986; Smith et al. 1990; Roberts et al. 1993). Spectral mixing occurs when materials with different spectral properties are represented by a single image pixel. Each image pixel contains a spectrum of reflectance values for all the wavebands in the imagery. These spectra may be considered as the sig- natures of the ground materials such as soil types, provided that the material occupies the whole pixel. Each pixel retains the characteristic features of the individual spectra from each of the component reflective materials. Spectral unmixing of satellite images is one of the most widely used methods for deriving information from mixed pixels (Lu et al. 2003). Spectral mixture analyses (SMA) are generally defined as the calculation of land cover area fraction within a pixel (Roberts et al. 1998). Spectral mix- ture analysis was developed for interpreting high spectral resolution advanced visible/infrared images data and was later expanded to be used with Landsat data (Lunetta 1998). The process involves the selection of representative pure spectra (end-member) and the unmixing of the spectral information of a pixel. To get more information from a single pixel, the proportions of these materials can be approximated using a spectral mixing model (Boardman 1994). The spectrum recorded in every image pixel is a linear combination A. M. Saleh (*) : A. B. Belal : S. M. Arafat Soil Sciences Department, National Authority for Remote Sensing and Space Sciences (NARSS), Cairo, Egypt e-mail: ahmed_ms@hotmail.com Arab J Geosci (2013) 6:1799–1806 DOI 10.1007/s12517-011-0501-6