A PLSR model to predict soil salinity using Sentinel-2 MSI data

Salinization is one of the most widespread environmental threats in arid and semi-arid regions that occur either naturally or artificially within the soil. When exceeding the thresholds, salinity becomes a severe danger, damaging agricultural production, water and soil quality, biodiversity, and inf...

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Autor principal:	Sahbeni Ghada
Formato:	article
Lenguaje:	EN
Publicado:	De Gruyter 2021
Materias:	soil salinity sentinel-2 msi plsr regression analysis multispectral remote sensing statistical modeling the great hungarian plain Geology QE1-996.5
Acceso en línea:	https://doaj.org/article/60ea53cca37d490a8266786652840568
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spelling	oai:doaj.org-article:60ea53cca37d490a82667866528405682021-12-05T14:10:49ZA PLSR model to predict soil salinity using Sentinel-2 MSI data2391-544710.1515/geo-2020-0286https://doaj.org/article/60ea53cca37d490a82667866528405682021-08-01T00:00:00Zhttps://doi.org/10.1515/geo-2020-0286https://doaj.org/toc/2391-5447Salinization is one of the most widespread environmental threats in arid and semi-arid regions that occur either naturally or artificially within the soil. When exceeding the thresholds, salinity becomes a severe danger, damaging agricultural production, water and soil quality, biodiversity, and infrastructures. This study used spectral indices, including salinity and vegetation indices, Sentinel-2 MSI original bands, and DEM, to model soil salinity in the Great Hungarian Plain. Eighty-one soil samples in the upper 30 cm of the soil surface were collected from vegetated and nonvegetated areas by the Research Institute for Soil Sciences and Agricultural Chemistry (RISSAC). The sampling campaign of salinity monitoring was performed in the dry season to enhance salt spectral characteristics during its accumulation in the subsoil. Hence, applying a partial least squares regression (PLSR) between salt content (g/kg) and remotely sensed data manifested a highly moderate correlation with a coefficient of determination R 2 of 0.68, a p-value of 0.000017, and a root mean square error of 0.22. The final model can be deployed to highlight soil salinity levels in the study area and assist in understanding the efficacy of land management strategies.Sahbeni GhadaDe Gruyterarticlesoil salinitysentinel-2 msiplsrregression analysismultispectral remote sensingstatistical modelingthe great hungarian plainGeologyQE1-996.5ENOpen Geosciences, Vol 13, Iss 1, Pp 977-987 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	soil salinity sentinel-2 msi plsr regression analysis multispectral remote sensing statistical modeling the great hungarian plain Geology QE1-996.5
spellingShingle	soil salinity sentinel-2 msi plsr regression analysis multispectral remote sensing statistical modeling the great hungarian plain Geology QE1-996.5 Sahbeni Ghada A PLSR model to predict soil salinity using Sentinel-2 MSI data
description	Salinization is one of the most widespread environmental threats in arid and semi-arid regions that occur either naturally or artificially within the soil. When exceeding the thresholds, salinity becomes a severe danger, damaging agricultural production, water and soil quality, biodiversity, and infrastructures. This study used spectral indices, including salinity and vegetation indices, Sentinel-2 MSI original bands, and DEM, to model soil salinity in the Great Hungarian Plain. Eighty-one soil samples in the upper 30 cm of the soil surface were collected from vegetated and nonvegetated areas by the Research Institute for Soil Sciences and Agricultural Chemistry (RISSAC). The sampling campaign of salinity monitoring was performed in the dry season to enhance salt spectral characteristics during its accumulation in the subsoil. Hence, applying a partial least squares regression (PLSR) between salt content (g/kg) and remotely sensed data manifested a highly moderate correlation with a coefficient of determination R 2 of 0.68, a p-value of 0.000017, and a root mean square error of 0.22. The final model can be deployed to highlight soil salinity levels in the study area and assist in understanding the efficacy of land management strategies.
format	article
author	Sahbeni Ghada
author_facet	Sahbeni Ghada
author_sort	Sahbeni Ghada
title	A PLSR model to predict soil salinity using Sentinel-2 MSI data
title_short	A PLSR model to predict soil salinity using Sentinel-2 MSI data
title_full	A PLSR model to predict soil salinity using Sentinel-2 MSI data
title_fullStr	A PLSR model to predict soil salinity using Sentinel-2 MSI data
title_full_unstemmed	A PLSR model to predict soil salinity using Sentinel-2 MSI data
title_sort	plsr model to predict soil salinity using sentinel-2 msi data
publisher	De Gruyter
publishDate	2021
url	https://doaj.org/article/60ea53cca37d490a8266786652840568
work_keys_str_mv	AT sahbenighada aplsrmodeltopredictsoilsalinityusingsentinel2msidata AT sahbenighada plsrmodeltopredictsoilsalinityusingsentinel2msidata
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A PLSR model to predict soil salinity using Sentinel-2 MSI data

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