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dc.contributor.authorKılıç, Orhan Mete
dc.contributor.authorBudak, Mesut
dc.contributor.authorGünal, Elif
dc.contributor.authorAcir, Nurullah
dc.contributor.authorHalbac-Cotoara-Zamfir, Rares
dc.contributor.authorAlfarraj, Saleh
dc.contributor.authorAnsari, Mohammad Javed
dc.date.accessioned2022-05-09T11:25:21Z
dc.date.available2022-05-09T11:25:21Z
dc.date.issued2022en_US
dc.identifier.citationKılıc, O. M., Budak, M., Gunal, E., Acır, N., Halbac-Cotoara-Zamfir, R., Alfarraj, S., & Ansari, M. J. (2022). Soil salinity assessment of a natural pasture using remote sensing techniques in central Anatolia, Turkey. Plos one, 17(4), e0266915.en_US
dc.identifier.issn19326203
dc.identifier.urihttps://doi.org/10.1371/journal.pone.0266915
dc.identifier.urihttps://hdl.handle.net/20.500.12513/4428
dc.description.abstractSoil salinity is a major land degradation process reducing biological productivity in arid and semi-arid regions. Therefore, its effective monitoring and management is inevitable. Recent developments in remote sensing technology have made it possible to accurately identify and effectively monitor soil salinity. Hence, this study determined salinity levels of surface soils in 2650 ha agricultural and natural pastureland located in an arid region of central Anatolia, Turkey. The relationship between electrical conductivity (EC) values of 145 soil samples and the dataset created using Landsat 5 TM satellite image was investigated. Remote sensing dataset for 23 variables, including visible, near infrared (NIR) and short-wave infrared (SWIR) spectral ranges, salinity, and vegetation indices were created. The highest correlation between EC values and remote sensing dataset was obtained in SWIR1 band (r = -0.43). Linear regression analysis was used to reveal the relationship between six bands and indices selected from the variables with the highest correlations. Coefficient of determination (R2 = 0.19) results indicated that models obtained using satellite image did not provide reliable results in determining soil salinity. Microtopography is the major factor affecting spatial distribution of soil salinity and caused heterogeneous distribution of salts on surface soils. Differences in salt content of soils caused heterogeneous distribution of halophytes and led to spectral complexity. The dark colored slickpots in small-scale depressions are common features of sodic soils, which are responsible for spectral complexity. In addition, low spatial resolution of Landsat 5 TM images is another reason decreasing the reliability of models in determining soil salinity. © 2022 Kilic et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.en_US
dc.language.isoengen_US
dc.publisherPublic Library of Scienceen_US
dc.relation.isversionof10.1371/journal.pone.0266915en_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectEnvironmental Monitoringen_US
dc.subjectRemote Sensing Technologyen_US
dc.subjectReproducibility of Resultsen_US
dc.subjectSalinityen_US
dc.subjectSoilen_US
dc.subjectTurkeyen_US
dc.titleSoil salinity assessment of a natural pasture using remote sensing techniques in central Anatolia, Turkeyen_US
dc.typearticleen_US
dc.relation.journalPlos Oneen_US
dc.contributor.departmentZiraat Fakültesien_US
dc.contributor.authorIDNurullah Acir / 0000-0001-7591-0496en_US
dc.identifier.volume17en_US
dc.identifier.issue4en_US
dc.identifier.startpage1en_US
dc.identifier.endpage14en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US


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