Remote sensing data can provide valuable information about the surface expression of regional geomorphologic and geological features of arid regions. In the present study, several processing techniques were applied to reveal such in the Qatar Peninsula. Those included preprocessing for radiometric and geometric correction, various enhancement methods, classification, accuracy assessment, contrast stretching, color composition, and principal component analyses. Those were coupled with field groundtruthing and lab analyses. Field groundtruthing included one hundred and forty measurements of spectral reflectance for various sediment exposures representing main sand types in the four studied parts in Qatar. Lab investigations included grain size analysis, X-ray diffraction and laboratory measurements of spectral reflectance. During the course of this study three sand types have been identified: (i) sabkha-derived salt-rich, quartz sand, and (ii) beach-derived calcareous sand and (iii) aeolian dune quartz. Those areas are spectrally distinct in the VNIR, suggesting that VNIR spectral data can be used to discriminate them. The study found that the main limitation of the ground spectral reflectance study is the difficulty of covering large areas. The study also found that ground and laboratory spectral radiance are generally higher in reflectance than those of Landsat TM. This is due to several factors such as atmospheric conditions, the low altitude or different scales. Whereas for areas with huge size of dune sand, the Landsat TM spectral has higher reflectance than those from field and laboratory. The study observed that there is a good correspondence or correlation of the wavelengths maximum sensitivity between the three spectral measurements i.e lab, field and space-borne measurements. © 2009 by the authors; licensee Molecular Diversity Preservation International, Basel, Switzerland.
CITATION STYLE
Sadiq, A., & Howari, F. (2009). Remote sensing and spectral characteristics of desert sand from Qatar Peninsula, Arabian/Persian Gulf. Remote Sensing, 1(4), 915–933. https://doi.org/10.3390/rs1040915
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