Potential of Landsat 8 OLI for mapping and monitoring of soil salinity in an arid region: A case study in Dushak, Turkmenistan.

Soil salinity is the most common land degradation agent that impairs soil functions, ecosystem services and negatively affects agricultural production in arid and semi-arid regions of the world. Therefore, reliable methods are needed to estimate spatial distribution of soil salinity for the manageme...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Elif Günal, Xiukang Wang, Orhan Mete Kılıc, Mesut Budak, Sami Al Obaid, Mohammad Javed Ansari, Marian Brestic
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/bd931cb0e55642fba7989973c6a53226
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:bd931cb0e55642fba7989973c6a53226
record_format	dspace
spelling	oai:doaj.org-article:bd931cb0e55642fba7989973c6a532262021-12-02T20:13:08ZPotential of Landsat 8 OLI for mapping and monitoring of soil salinity in an arid region: A case study in Dushak, Turkmenistan.1932-620310.1371/journal.pone.0259695https://doaj.org/article/bd931cb0e55642fba7989973c6a532262021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0259695https://doaj.org/toc/1932-6203Soil salinity is the most common land degradation agent that impairs soil functions, ecosystem services and negatively affects agricultural production in arid and semi-arid regions of the world. Therefore, reliable methods are needed to estimate spatial distribution of soil salinity for the management, remediation, monitoring and utilization of saline soils. This study investigated the potential of Landsat 8 OLI satellite data and vegetation, soil salinity and moisture indices in estimating surface salinity of 1014.6 ha agricultural land located in Dushak, Turkmenistan. Linear regression model was developed between land measurements and remotely sensed indicators. A systematic regular grid-sampling method was used to collect 50 soil samples from 0-20 cm depth. Sixteen indices were extracted from Landsat-8 OLI satellite images. Simple and multivariate regression models were developed between the measured electrical conductivity values and the remotely sensed indicators. The highest correlation between remote sensing indicators and soil EC values in determining soil salinity was calculated in SAVI index (r = 0.54). The reliability indicated by R2 value (0.29) of regression model developed with the SAVI index was low. Therefore, new model was developed by selecting the indicators that can be included in the multiple regression model from the remote sensing indicators. A significant (r = 0.74) correlation was obtained between the multivariate regression model and soil EC values, and salinity was successfully mapped at a moderate level (R2: 0.55). The classification of the salinity map showed that 21.71% of the field was non-saline, 29.78% slightly saline, 31.40% moderately saline, 15.25% strongly saline and 1.44% very strongly. The results revealed that multivariate regression models with the help of Landsat 8 OLI satellite images and indices obtained from the images can be used for modeling and mapping soil salinity of small-scale lands.Elif GünalXiukang WangOrhan Mete KılıcMesut BudakSami Al ObaidMohammad Javed AnsariMarian BresticPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 11, p e0259695 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Elif Günal Xiukang Wang Orhan Mete Kılıc Mesut Budak Sami Al Obaid Mohammad Javed Ansari Marian Brestic Potential of Landsat 8 OLI for mapping and monitoring of soil salinity in an arid region: A case study in Dushak, Turkmenistan.
description	Soil salinity is the most common land degradation agent that impairs soil functions, ecosystem services and negatively affects agricultural production in arid and semi-arid regions of the world. Therefore, reliable methods are needed to estimate spatial distribution of soil salinity for the management, remediation, monitoring and utilization of saline soils. This study investigated the potential of Landsat 8 OLI satellite data and vegetation, soil salinity and moisture indices in estimating surface salinity of 1014.6 ha agricultural land located in Dushak, Turkmenistan. Linear regression model was developed between land measurements and remotely sensed indicators. A systematic regular grid-sampling method was used to collect 50 soil samples from 0-20 cm depth. Sixteen indices were extracted from Landsat-8 OLI satellite images. Simple and multivariate regression models were developed between the measured electrical conductivity values and the remotely sensed indicators. The highest correlation between remote sensing indicators and soil EC values in determining soil salinity was calculated in SAVI index (r = 0.54). The reliability indicated by R2 value (0.29) of regression model developed with the SAVI index was low. Therefore, new model was developed by selecting the indicators that can be included in the multiple regression model from the remote sensing indicators. A significant (r = 0.74) correlation was obtained between the multivariate regression model and soil EC values, and salinity was successfully mapped at a moderate level (R2: 0.55). The classification of the salinity map showed that 21.71% of the field was non-saline, 29.78% slightly saline, 31.40% moderately saline, 15.25% strongly saline and 1.44% very strongly. The results revealed that multivariate regression models with the help of Landsat 8 OLI satellite images and indices obtained from the images can be used for modeling and mapping soil salinity of small-scale lands.
format	article
author	Elif Günal Xiukang Wang Orhan Mete Kılıc Mesut Budak Sami Al Obaid Mohammad Javed Ansari Marian Brestic
author_facet	Elif Günal Xiukang Wang Orhan Mete Kılıc Mesut Budak Sami Al Obaid Mohammad Javed Ansari Marian Brestic
author_sort	Elif Günal
title	Potential of Landsat 8 OLI for mapping and monitoring of soil salinity in an arid region: A case study in Dushak, Turkmenistan.
title_short	Potential of Landsat 8 OLI for mapping and monitoring of soil salinity in an arid region: A case study in Dushak, Turkmenistan.
title_full	Potential of Landsat 8 OLI for mapping and monitoring of soil salinity in an arid region: A case study in Dushak, Turkmenistan.
title_fullStr	Potential of Landsat 8 OLI for mapping and monitoring of soil salinity in an arid region: A case study in Dushak, Turkmenistan.
title_full_unstemmed	Potential of Landsat 8 OLI for mapping and monitoring of soil salinity in an arid region: A case study in Dushak, Turkmenistan.
title_sort	potential of landsat 8 oli for mapping and monitoring of soil salinity in an arid region: a case study in dushak, turkmenistan.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/bd931cb0e55642fba7989973c6a53226
work_keys_str_mv	AT elifgunal potentialoflandsat8oliformappingandmonitoringofsoilsalinityinanaridregionacasestudyindushakturkmenistan AT xiukangwang potentialoflandsat8oliformappingandmonitoringofsoilsalinityinanaridregionacasestudyindushakturkmenistan AT orhanmetekılıc potentialoflandsat8oliformappingandmonitoringofsoilsalinityinanaridregionacasestudyindushakturkmenistan AT mesutbudak potentialoflandsat8oliformappingandmonitoringofsoilsalinityinanaridregionacasestudyindushakturkmenistan AT samialobaid potentialoflandsat8oliformappingandmonitoringofsoilsalinityinanaridregionacasestudyindushakturkmenistan AT mohammadjavedansari potentialoflandsat8oliformappingandmonitoringofsoilsalinityinanaridregionacasestudyindushakturkmenistan AT marianbrestic potentialoflandsat8oliformappingandmonitoringofsoilsalinityinanaridregionacasestudyindushakturkmenistan
_version_	1718374847649677312

Potential of Landsat 8 OLI for mapping and monitoring of soil salinity in an arid region: A case study in Dushak, Turkmenistan.

Ejemplares similares