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Soil salinity assessment using vegetation indices derived from Sentinel-2 multispectral data. application to Lezíria Grande, Portugal

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dc.contributor.authorRamos, Tiago B.
dc.contributor.authorCastanheira, Nádia
dc.contributor.authorOliveira, Ana R.
dc.contributor.authorPaz, Ana Marta
dc.contributor.authorDarouich, Hanaa
dc.contributor.authorSimionesei, Lucian
dc.contributor.authorFarzamian, Mohammad
dc.contributor.authorGonçalves, Maria C.
dc.date.accessioned2021-09-24T15:06:00Z
dc.date.available2021-09-24T15:06:00Z
dc.date.issued2020
dc.description.abstractLezíria Grande is an important agricultural area in Portugal, prone to waterlogging and salinity problems due to the influence of estuarine tides on groundwater dynamics. Simple, non-invasive, practical approaches are need for monitoring soil salinity in the region and preventing further degradation of soil resources. The objective of this study was to develop regression models for soil salinity assessment in Lezíria Grande based on the relationship between multi-year crop reflectance data derived from Sentinel-2 multispectral imagery and rootzone salinity. Nine vegetation indices (VI), computed from the annual averages of the spectral bands, were tested between 2017 and 2019. The multi-year maximum from each pixel was then used for correlating the VI with the ground-truth dataset. This dataset was composed of average values of the electrical conductivity of the soil saturation paste extract (ECe mean) measured in 80 sampling sites (0–1.5 m depth) located in four agricultural fields representative of the salinity gradient in the region. The Canopy Response Salinity Index (CRSI), which uses the blue (490 nm), green (560 nm), red (665 nm), and infrared (842 nm) bands, provided the strongest correlation with measured data (r=−0.787). Regression models further considered vegetation cover and soil type as explanatory variables, with predictions resulting in a coefficient of determination (R2) ranging from 0.63 to 0.91 and a root mean square error (RMSE) varying from 1.63 to 3.26 dS m−1. The use of remote sensing data for soil salinity assessment showed to be an interesting option to consider in future soil monitoring programs. Nevertheless, more detailed covariates are needed for improving salinity assessment models at the regional scalept_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationAgricultural Water Management 241 (2020) 106387pt_PT
dc.identifier.doihttps://doi.org/10.1016/j.agwat.2020.106387pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.5/22004
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherElsevierpt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectcanopy response salinity indexpt_PT
dc.subjectsoil-environmental covariatespt_PT
dc.subjectmulti-year maximumpt_PT
dc.subjectmultiple regression analysispt_PT
dc.subjectrootzone salinitypt_PT
dc.titleSoil salinity assessment using vegetation indices derived from Sentinel-2 multispectral data. application to Lezíria Grande, Portugalpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardNumber150010
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/3599-PPCDT/150010/PT
oaire.citation.titleAgricultural Water Managementpt_PT
oaire.fundingStream3599-PPCDT
person.familyNameDarouich
person.givenNameHanaa
person.identifier.ciencia-id5D13-DECD-D29D
person.identifier.orcid0000-0001-8811-1175
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsembargoedAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication2f3da168-54c0-4b69-8d21-0db117ba3530
relation.isAuthorOfPublication.latestForDiscovery2f3da168-54c0-4b69-8d21-0db117ba3530
relation.isProjectOfPublication590f4a8e-7cf7-4dec-821a-54d2a37183b1
relation.isProjectOfPublication.latestForDiscovery590f4a8e-7cf7-4dec-821a-54d2a37183b1

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