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dc.contributorIbrakhimov, Mirzakhayoten_US
dc.contributorAkramkhanov, Akmalen_US
dc.contributorBauer, Christianen_US
dc.contributorConrad, Christopheren_US
dc.creatorSultanov, Murodjonen_US
dc.identifier.citationMurodjon Sultanov, Mirzakhayot Ibrakhimov, Akmal Akramkhanov, Christian Bauer, Christopher Conrad. (7/2/2019). Modelling end‑of‑season soil salinity in irrigated agriculture through multi‑temporal optical remote sensing, environmental parameters, and in situ information. Journal of Photogrammetry Remote Sensing and Geoinformation Science, 86, pp. 221-233.en_US
dc.description.abstractAccurate information of soil salinity levels enables for remediation actions in long-term operating irrigation systems with malfunctioning drainage and shallow groundwater (GW), as they are widespread throughout the Aral Sea Basin (ASB). Multi-temporal Landsat 5 data combined with GW levels and potentials, elevation and relative topographic position, and soil (clay content) parameters, were used for modelling bulk electromagnetic induction (EMI) at the end of the irrigation season. Random forest (RF) regressionwas applied to predict in situ observations of 2008–2011 which originated from a cotton research station in Uzbekistan. Validation, i.e. median statistics from 100 RF runs with a holdout of each 20% of the samples, revealed that mono-temporal (R2: 0.1–0.18, RMSE: 16.7–24.9 mSm−1) underperformed multi-temporal RS data (R2: 0.29–0.45; RMSE: 15.1–20.9 mSm−1). Combinations of multi-temporal RS data with environmental parameters achieved highest accuracies (R2: 0.36–0.50, RMSE: 13.2–19.9 mSm−1). Beside RS data recorded at the initial peaks of the major irrigation phases, terrain and GW parameters turned out to be important variables for the model. RF preferred neither raw data nor spectral indices known to be suitable for detecting soil salinity. Unexplained variance components result from missing environmental variables, but also from processes not considered in the data. A calibration of the EMI for electrical conductivity and the standard soil salinity classification returned an overall accuracy of 76–83% for the period 2008–2011. The presented indirect approach together with the in situ calibration of the EMI data can support an accurate mapping of soil salinity at the end of the season, at least in the type of irrigation systems found in the ASB.en_US
dc.publisherSpringer International Publishing AGen_US
dc.sourceJournal of Photogrammetry Remote Sensing and Geoinformation Science;86,(2019) Pagination 221-233en_US
dc.subjectelectromagnetic inductionen_US
dc.subjectirrigated agricultureen_US
dc.subjectenvironmental parametersen_US
dc.titleModelling end‑of‑season soil salinity in irrigated agriculture through multi‑temporal optical remote sensing, environmental parameters, and in situ informationen_US
dc.typeJournal Articleen_US
cg.creator.idAkramkhanov, Akmal: 0000-0002-4316-5580en_US
cg.subject.agrovocsoil salinityen_US
cg.contributor.centerInternational Center for Agricultural Research in the Dry Areas - ICARDAen_US
cg.contributor.centerKhorezm Rural Advisory Support Service - KRASSen_US
cg.contributor.centerUniversity of Wuerzburgen_US
cg.contributor.centerUniversity of Wuerzburg, Institute of Geography and Geology - Uni-Wuerzburg - Geographieen_US
cg.contributor.centerMartin-Luther-University Halle-Wittenberg, Faculty of Sciences III, Institute of Geosciences and Geography - Uni-Halle - NATFAK 3- GEOen_US
cg.contributor.funderVolkswagen Foundationen_US
cg.contributor.projectCommunication and Documentation Information Services (CODIS)en_US
cg.contributor.project-lead-instituteInternational Center for Agricultural Research in the Dry Areas - ICARDAen_US
cg.coverage.regionCentral Asiaen_US
cg.isijournalISI Journalen_US
dc.identifier.statusTimeless limited accessen_US
cg.journalJournal of Photogrammetry Remote Sensing and Geoinformation Scienceen_US

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