Detection and modeling of soil salinity variations in arid lands using remote sensing data

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...

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Autores principales:	Alqasemi Abduldaem S., Ibrahim Majed, Fadhil Al-Quraishi Ayad M., Saibi Hakim, Al-Fugara A’kif, Kaplan Gordana
Formato:	article
Lenguaje:	EN
Publicado:	De Gruyter 2021
Materias:	electrical conductivity remote sensing landsat 8 salinity salinization spectral index lst Geology QE1-996.5
Acceso en línea:	https://doaj.org/article/e895e95921d541aabedfc38b725ef83c
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spelling	oai:doaj.org-article:e895e95921d541aabedfc38b725ef83c2021-12-05T14:10:48ZDetection and modeling of soil salinity variations in arid lands using remote sensing data2391-544710.1515/geo-2020-0244https://doaj.org/article/e895e95921d541aabedfc38b725ef83c2021-04-01T00:00:00Zhttps://doi.org/10.1515/geo-2020-0244https://doaj.org/toc/2391-5447Soil 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.Alqasemi Abduldaem S.Ibrahim MajedFadhil Al-Quraishi Ayad M.Saibi HakimAl-Fugara A’kifKaplan GordanaDe Gruyterarticleelectrical conductivityremote sensinglandsat 8salinity salinizationspectral indexlstGeologyQE1-996.5ENOpen Geosciences, Vol 13, Iss 1, Pp 443-453 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	electrical conductivity remote sensing landsat 8 salinity salinization spectral index lst Geology QE1-996.5
spellingShingle	electrical conductivity remote sensing landsat 8 salinity salinization spectral index lst Geology QE1-996.5 Alqasemi Abduldaem S. Ibrahim Majed Fadhil Al-Quraishi Ayad M. Saibi Hakim Al-Fugara A’kif Kaplan Gordana Detection and modeling of soil salinity variations in arid lands using remote sensing data
description	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.
format	article
author	Alqasemi Abduldaem S. Ibrahim Majed Fadhil Al-Quraishi Ayad M. Saibi Hakim Al-Fugara A’kif Kaplan Gordana
author_facet	Alqasemi Abduldaem S. Ibrahim Majed Fadhil Al-Quraishi Ayad M. Saibi Hakim Al-Fugara A’kif Kaplan Gordana
author_sort	Alqasemi Abduldaem S.
title	Detection and modeling of soil salinity variations in arid lands using remote sensing data
title_short	Detection and modeling of soil salinity variations in arid lands using remote sensing data
title_full	Detection and modeling of soil salinity variations in arid lands using remote sensing data
title_fullStr	Detection and modeling of soil salinity variations in arid lands using remote sensing data
title_full_unstemmed	Detection and modeling of soil salinity variations in arid lands using remote sensing data
title_sort	detection and modeling of soil salinity variations in arid lands using remote sensing data
publisher	De Gruyter
publishDate	2021
url	https://doaj.org/article/e895e95921d541aabedfc38b725ef83c
work_keys_str_mv	AT alqasemiabduldaems detectionandmodelingofsoilsalinityvariationsinaridlandsusingremotesensingdata AT ibrahimmajed detectionandmodelingofsoilsalinityvariationsinaridlandsusingremotesensingdata AT fadhilalquraishiayadm detectionandmodelingofsoilsalinityvariationsinaridlandsusingremotesensingdata AT saibihakim detectionandmodelingofsoilsalinityvariationsinaridlandsusingremotesensingdata AT alfugaraakif detectionandmodelingofsoilsalinityvariationsinaridlandsusingremotesensingdata AT kaplangordana detectionandmodelingofsoilsalinityvariationsinaridlandsusingremotesensingdata
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Detection and modeling of soil salinity variations in arid lands using remote sensing data

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