Soil moisture inversion via semiempirical and machine learning methods with full-polarization radarsat-2 and polarimetric target decomposition data: A comparative study

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dc.authorid0000-0001-5127-4632en_US
dc.authorid0000-0002-9368-8902en_US
dc.contributor.authorAcar, Hüseyin
dc.contributor.authorÖzerdem, Mehmet Siraç
dc.contributor.authorAcar, Emrullah
dc.date.accessioned2021-07-27T13:39:11Z
dc.date.available2021-07-27T13:39:11Z
dc.date.issued2020en_US
dc.departmentDicle Üniversitesi, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.description.abstractIn this article, surface soil moisture was retrieved from Radarsat-2 and polarimetric target decomposition data by using semiempirical models and machine learning methods. The semiempirical models and machine learning techniques employed were Oh (1992), Dubois (1995), Oh (2004) and Generalized Regression Neural Network (GRNN), Least Squares - Support Vector Machine (LS-SVM), Extreme Learning Machine (ELM), Kernel based Extreme Learning Machine (KELM), Adaptive Network based Fuzzy Inference System (ANFIS), respectively. In addition, Yamaguchi, van Zyl, Freeman-Durden, H/A/a and Cloude polarimetric target decomposition methods were used in this study. For soil moisture inversion, firstly, preprocessing was applied to the Radarsat-2 image of two different dates with bare and moderately vegetated soil. Then, sigma nought coefficients and the polarimetric decomposition components were extracted as feature vector from preprocessed SAR image pixels corresponding to ground measured points. Lastly, sigma nought coefficients were used in semiempirical inversion models, and sigma nought coefficients and polarimetric decomposition components were used as input to machine learning methods. The best accuracy results for semiempirical models were 13.01 vol. % and 17.91 vol. % Root Mean Square Error (RMSE) for bare and moderately vegetated soil, respectively. The best accuracy for machine learning techniques were 4.04 vol. % and 2.72 vol. % RMSE for two dates, respectively. The results indicated that the machine learning techniques performed much better than the semiempirical models.en_US
dc.identifier.citationAcar, H., Özerdem, M. S. ve Acar, E. (2020). Soil moisture inversion via semiempirical and machine learning methods with full-polarization radarsat-2 and polarimetric target decomposition data: A comparative study. IEEE Access, 8, 197896-197907.en_US
dc.identifier.doi10.1109/ACCESS.2020.3035235
dc.identifier.endpage197907en_US
dc.identifier.issn2169-3536
dc.identifier.scopus2-s2.0-85102783591
dc.identifier.scopusqualityQ1
dc.identifier.startpage197896en_US
dc.identifier.urihttps://ieeexplore.ieee.org/document/9246504
dc.identifier.urihttps://hdl.handle.net/11468/7248
dc.identifier.volume8en_US
dc.identifier.wosWOS:000589765200001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorAcar, Hüseyin
dc.institutionauthorÖzerdem, Mehmet Siraç
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofIEEE Access
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectMachine learningen_US
dc.subjectPolarimetric target decompositionsen_US
dc.subjectRadarsat-2 imageryen_US
dc.subjectRemote sensingen_US
dc.subjectSemiempirical modelsen_US
dc.subjectSoil moisture inversionen_US
dc.titleSoil moisture inversion via semiempirical and machine learning methods with full-polarization radarsat-2 and polarimetric target decomposition data: A comparative studyen_US
dc.titleSoil moisture inversion via semiempirical and machine learning methods with full-polarization radarsat-2 and polarimetric target decomposition data: A comparative study
dc.typeArticleen_US

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