Soil moisture estimation over vegetated agricultural areas: Tigris Basin, Turkey from Radarsat-2 data by polarimetric decomposition models and a generalized regression neural network

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dc.authorid0000-0002-9368-8902en_US
dc.authorid0000-0002-1897-9830en_US
dc.authorid0000-0003-4165-6631en_US
dc.contributor.authorÖzerdem, Mehmet Siraç
dc.contributor.authorAcar, Emrullah
dc.contributor.authorEkinci, Remzi
dc.date.accessioned2024-03-29T12:48:53Z
dc.date.available2024-03-29T12:48:53Z
dc.date.issued2017en_US
dc.departmentDicle Üniversitesi, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.description.abstractDetermining the soil moisture in agricultural fields is a significant parameter to use irrigation systems efficiently. In contrast to standard soil moisture measurements, good results might be acquired in a shorter time over large areas by remote sensing tools. In order to estimate the soil moisture over vegetated agricultural areas, a relationship between Radarsat-2 data and measured ground soil moistures was established by polarimetric decomposition models and a generalized regression neural network (GRNN). The experiments were executed over two agricultural sites on the Tigris Basin, Turkey. The study consists of four phases. In the first stage, Radarsat-2 data were acquired on different dates and in situ measurements were implemented simultaneously. In the second phase, the Radarsat-2 data were pre-processed and the GPS coordinates of the soil sample points were imported to this data. Then the standard sigma backscattering coefficients with the Freeman–Durden and H/A/α polarimetric decomposition models were employed for feature extraction and a feature vector with four sigma backscattering coefficients (σhh, σhv, σvh, and σvv) and six polarimetric decomposition parameters (entropy, anisotropy, alpha angle, volume scattering, odd bounce, and double bounce) were generated for each pattern. In the last stage, GRNN was used to estimate the regional soil moisture with the aid of feature vectors. The results indicated that radar is a strong remote sensing tool for soil moisture estimation, with mean absolute errors around 2.31 vol %, 2.11 vol %, and 2.10 vol % for Datasets 1–3, respectively; and 2.46 vol %, 2.70 vol %, 7.09 vol %, and 5.70 vol % on Datasets 1 & 2, 2 & 3, 1 & 3, and 1 & 2 & 3, respectively.en_US
dc.identifier.citationÖzerdem, M. S., Acar, E. ve Ekinci, R. (2017). Soil moisture estimation over vegetated agricultural areas: Tigris Basin, Turkey from Radarsat-2 data by polarimetric decomposition models and a generalized regression neural network. Remote Sensing, 9(4), 1-21.en_US
dc.identifier.doi10.3390/rs9040395
dc.identifier.endpage21en_US
dc.identifier.issn2072-4292
dc.identifier.issue4en_US
dc.identifier.scopus2-s2.0-85041237675
dc.identifier.scopusqualityQ1
dc.identifier.startpage1en_US
dc.identifier.urihttps://www.mdpi.com/2072-4292/9/4/395
dc.identifier.urihttps://hdl.handle.net/11468/13783
dc.identifier.volume9en_US
dc.identifier.wosWOS:000402571700094
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorÖzerdem, Mehmet Siraç
dc.institutionauthorEkinci, Remzi
dc.language.isoenen_US
dc.publisherMDPIen_US
dc.relation.ispartofRemote Sensing
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.relation.tubitak114E543
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectFeature extractionen_US
dc.subjectFreeman–Durdenen_US
dc.subjectGRNNen_US
dc.subjectH/A/αen_US
dc.subjectMachine learningen_US
dc.subjectPolarimetric decompositionen_US
dc.subjectRadarsat-2en_US
dc.subjectRemote sensingen_US
dc.subjectSoil moistureen_US
dc.titleSoil moisture estimation over vegetated agricultural areas: Tigris Basin, Turkey from Radarsat-2 data by polarimetric decomposition models and a generalized regression neural networken_US
dc.titleSoil moisture estimation over vegetated agricultural areas: Tigris Basin, Turkey from Radarsat-2 data by polarimetric decomposition models and a generalized regression neural network
dc.typeArticleen_US

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