19 August 2017 Optical tool for salinity detection by remote sensing spectroscopy: application on Oran watershed, Algeria
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Soil salinity is a complex problem that affects groundwater aquifers and agricultural lands in the semiarid regions. Remote sensing and spectroscopy database systems provide accuracy for salinity autodetection and dynamical delineation. Salinity detection techniques using polychromatic wavebands by field geocomputation and experimental data are time consuming and expensive. This paper presents an automated spectral detection and identification of salt minerals using a monochromatic waveband concept from multispectral bands—Landsat 8 Operational Land Imager (OLI) and Thermal InfraRed Sensor (TIRS) and spectroscopy United States Geological Survey database. For detecting mineral salts related to electrolytes, such as electronical and vibrational transitions, an integrated approach of salinity detection related to the optical monochromatic concept has been addressed. The purpose of this paper is to discriminate waveband intrinsic spectral similarity using the Beer–Lambert and Van 't Hoff laws for spectral curve extraction such as transmittance, reflectance, absorbance, land surface temperature, molar concentration, and osmotic pressure. These parameters are primordial for hydrodynamic salinity modeling and continuity identification using chemical and physical approaches. The established regression fitted models have been addressed for salt spectroscopy validation for suitable calibration and validation. Furthermore, our analytical tool is conducted for better decision interface using spectral salinity detection and identification in the Oran watershed, Algeria.
© 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)
Dehni Abdellatif, Dehni Abdellatif, Lounis Mourad, Lounis Mourad, } "Optical tool for salinity detection by remote sensing spectroscopy: application on Oran watershed, Algeria," Journal of Applied Remote Sensing 11(3), 036010 (19 August 2017). https://doi.org/10.1117/1.JRS.11.036010 . Submission: Received: 19 March 2017; Accepted: 18 July 2017
Received: 19 March 2017; Accepted: 18 July 2017; Published: 19 August 2017

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