Journal cover Journal topic
Geoscientific Instrumentation, Methods and Data Systems An interactive open-access journal of the European Geosciences Union
Geosci. Instrum. Method. Data Syst., 6, 149-158, 2017
http://www.geosci-instrum-method-data-syst.net/6/149/2017/
doi:10.5194/gi-6-149-2017
© Author(s) 2017. This work is distributed
under the Creative Commons Attribution 3.0 License.
Research article
15 Mar 2017
Soil salinity mapping and hydrological drought indices assessment in arid environments based on remote sensing techniques
Mohamed Elhag and Jarbou A. Bahrawi Department of Hydrology and Water Resources Management, Faculty of Meteorology, Environment & Arid Land Agriculture, King Abdulaziz University, Jeddah 21589, Saudi Arabia
Abstract. Vegetation indices are mostly described as crop water derivatives. The normalized difference vegetation index (NDVI) is one of the oldest remote sensing applications that is widely used to evaluate crop vigor directly and crop water relationships indirectly. Recently, several NDVI derivatives were exclusively used to assess crop water relationships. Four hydrological drought indices are examined in the current research study. The water supply vegetation index (WSVI), the soil-adjusted vegetation index (SAVI), the moisture stress index (MSI) and the normalized difference infrared index (NDII) are implemented in the current study as an indirect tool to map the effect of different soil salinity levels on crop water stress in arid environments. In arid environments, such as Saudi Arabia, water resources are under pressure, especially groundwater levels. Groundwater wells are rapidly depleted due to the heavy abstraction of the reserved water. Heavy abstractions of groundwater, which exceed crop water requirements in most of the cases, are powered by high evaporation rates in the designated study area because of the long days of extremely hot summer. Landsat 8 OLI data were extensively used in the current research to obtain several vegetation indices in response to soil salinity in Wadi ad-Dawasir. Principal component analyses (PCA) and artificial neural network (ANN) analyses are complementary tools used to understand the regression pattern of the hydrological drought indices in the designated study area.

Citation: Elhag, M. and Bahrawi, J. A.: Soil salinity mapping and hydrological drought indices assessment in arid environments based on remote sensing techniques, Geosci. Instrum. Method. Data Syst., 6, 149-158, doi:10.5194/gi-6-149-2017, 2017.
Publications Copernicus
Download
Short summary
The current work is aimed at the quantification of the hydrological drought indices' response to soil salinity. Work has been done to overcome the problems of soil salinity on a large scale for better water resource management, especially in arid environments.
The current work is aimed at the quantification of the hydrological drought indices' response to...
Share