Combining remote sensing and modeling approaches to assess soil salinity in irrigated areas of the Aral Sea Basin

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cg.contributor.affiliationInternational Water Management Instituteen_US
cg.coverage.countryUzbekistanen_US
cg.coverage.iso3166-alpha2UZen_US
cg.coverage.regionCentral Asiaen_US
cg.creator.identifierKakhramon Djumaboev: 0000-0002-0061-3603en_US
cg.identifier.doihttps://doi.org/10.29258/cajwr/2019-r1.v5-2/64-81engen_US
cg.identifier.urlhttps://cloud.mail.ru/public/25iy/4eATZdfpwen_US
cg.issn2522-9060en_US
cg.issue2en_US
cg.journalCentral Asian Journal of Water Researchen_US
cg.reviewStatusPeer Reviewen_US
cg.volume5en_US
dc.contributor.authorIbrakhimov, M.en_US
dc.contributor.authorAwan, U. K.en_US
dc.contributor.authorSultanov, M.en_US
dc.contributor.authorAkramkhanov, A.en_US
dc.contributor.authorDjumaboev, Kakhramonen_US
dc.contributor.authorConrad, C.en_US
dc.contributor.authorLamers, J.en_US
dc.date.accessioned2020-06-15T05:36:58Zen_US
dc.date.available2020-06-15T05:36:58Zen_US
dc.identifier.urihttps://hdl.handle.net/10568/108474en_US
dc.titleCombining remote sensing and modeling approaches to assess soil salinity in irrigated areas of the Aral Sea Basinen_US
dcterms.abstractAccurate assessment of the soil salinization is an important step for mitigation of agricultural land degradation. Remote sensing (RS) is widely used for salinity assessment, but knowledge on prediction precision is lacking. A RS-based salinity assessment in Khorezm allows for modest reliable prediction with weak (R2=0.15–0.29) relationship of the salinity maps produced with RS and interpolation of electromagnetic EM38 during growth periods and more reliable (R2=0.35–0.56) beyond irrigation periods. Modeling with HYDRUS-1D at slightly, moderately and highly saline sites at various depths showed that irrigation forces salts to move to deeper layers: salts reappear in the upper profile during dry periods. Beyond irrigation events, salts gradually accumulated in the upper soil layers without fluctuations. Coupling RS techniques with numerical modeling provided better insight into salinity dynamics than any of these approaches alone. This should be of interest to farmers and policy makers since the combination of methods will allow for better planning and management.en_US
dcterms.accessRightsOpen Accessen_US
dcterms.available2020-05-27en_US
dcterms.bibliographicCitationIbrakhimov, M.; Awan, U. K.; Sultanov, M.; Akramkhanov, A.; Djumaboev, Kakhramon; Conrad, C.; Lamers, J. 2019. Combining remote sensing and modeling approaches to assess soil salinity in irrigated areas of the Aral Sea Basin. Central Asian Journal of Water Research, 5(2):100-116. [doi: https://doi.org/10.29258/CAJWR/2019-R1.v5-2/64-81eng]en_US
dcterms.extentp. 64-81en_US
dcterms.issued2020-05-27en_US
dcterms.languageenen_US
dcterms.licenseCopyrighted; all rights reserveden_US
dcterms.publisherKazakh-German Universityen_US
dcterms.subjectsoil salinizationen_US
dcterms.subjectirrigated landen_US
dcterms.subjectremote sensingen_US
dcterms.subjectmodellingen_US
dcterms.subjectforecastingen_US
dcterms.subjecttechniquesen_US
dcterms.subjectsoil profilesen_US
dcterms.subjectgroundwateren_US
dcterms.subjectirrigated farmingen_US
dcterms.subjectcottonen_US
dcterms.subjectcase studiesen_US
dcterms.typeJournal Articleen_US

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