Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13087/152
Title: Detection and modeling of soil salinity variations in arid lands using remote sensing data
Authors: Alqasemi, Abduldaem S.
İbrahim, Majed
Al-Quraishi, Ayad M. Fadhil
Saibi, Hakim
Al-Fugara, A'kif
Kaplan, Gordana
Keywords: electrical conductivity
remote sensing
Landsat 8
salinity salinization
spectral index
LST
Issue Date: 2021
Publisher: De Gruyter Poland Sp Z O O
Abstract: Soil salinization is a ubiquitous global problem. The literature supports the integration of remote sensing (RS) techniques and field measurements as effective methods for developing soil salinity prediction models. The objectives of this study were to (i) estimate the level of soil salinity in Abu Dhabi using spectral indices and field measurements and (ii) develop a model for detecting and mapping soil salinity variations in the study area using RS data. We integrated Landsat 8 data with the electrical conductivity measurements of soil samples taken from the study area. Statistical analysis of the integrated data showed that the normalized difference vegetation index and bare soil index showed moderate correlations among the examined indices. The relation between these two indices can contribute to the development of successful soil salinity prediction models. Results show that 31% of the soil in the study area is moderately saline and 46% of the soil is highly saline. The results support that geoinformatic techniques using RS data and technologies constitute an effective tool for detecting soil salinity by modeling and mapping the spatial distribution of saline soils. Furthermore, we observed a low correlation between soil salinity and the nighttime land surface temperature.
URI: https://doi.org/10.1515/geo-2020-0244
https://hdl.handle.net/20.500.13087/152
ISSN: 2391-5447
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu
WoS İndeksli Yayınlar Koleksiyonu
Çevre Mühendisliği Bölümü Koleksiyonu

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