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Abstract
The water cloud model (WCM) can be inverted to estimate leaf area index (LAI) using the intensity of backscatter from synthetic aperture radar (SAR) sensors. Published studies have demonstrated that the WCM can accurately estimate LAI if the model is effectively calibrated. However, calibration of this model requires access to field measures of LAI as well as soil moisture. In contrast, machine learning (ML) algorithms can be trained to estimate LAI from [ver mas...]
dc.contributor.authorHosseini, Mehdi
dc.contributor.authorMcNairn, Heather
dc.contributor.authorMitchell, Scott
dc.contributor.authorRobertson, Laura Dingle
dc.contributor.authorDavidson, Andrew
dc.contributor.authorAhmadian, Nima
dc.contributor.authorBhattacharya, Avik
dc.contributor.authorBorg, Erik
dc.contributor.authorConrad, Christopher
dc.contributor.authorDabrowska Zielinska, Katarzyna
dc.contributor.authorDe Abelleyra, Diego
dc.contributor.authorGurdak, Radoslaw
dc.contributor.authorKumar, Vineet
dc.contributor.authorKussul, Nataliia
dc.contributor.authorMandal, Dipankar
dc.contributor.authorRao, Y.S.
dc.contributor.authorSaliendra, Nicanor
dc.contributor.authorShelestov, Andrii
dc.contributor.authorSpengler, Daniel
dc.contributor.authorVeron, Santiago Ramón
dc.contributor.authorHomayouni, Saeid
dc.contributor.authorBecker Reshef, Inbal
dc.date.accessioned2022-11-02T10:53:32Z
dc.date.available2022-11-02T10:53:32Z
dc.date.issued2021-04-01
dc.identifier.issn2072-4292
dc.identifier.otherhttps://doi.org/10.3390/rs13071348
dc.identifier.urihttp://hdl.handle.net/20.500.12123/13284
dc.identifier.urihttps://www.mdpi.com/2072-4292/13/7/1348
dc.description.abstractThe water cloud model (WCM) can be inverted to estimate leaf area index (LAI) using the intensity of backscatter from synthetic aperture radar (SAR) sensors. Published studies have demonstrated that the WCM can accurately estimate LAI if the model is effectively calibrated. However, calibration of this model requires access to field measures of LAI as well as soil moisture. In contrast, machine learning (ML) algorithms can be trained to estimate LAI from satellite data, even if field moisture measures are not available. In this study, a support vector machine (SVM) was trained to estimate the LAI for corn, soybeans, rice, and wheat crops. These results were compared to LAI estimates from the WCM. To complete this comparison, in situ and satellite data were collected from seven Joint Experiment for Crop Assessment and Monitoring (JECAM) sites located in Argentina, Canada, Germany, India, Poland, Ukraine and the United States of America (U.S.A.). The models used C-Band backscatter intensity for two polarizations (like-polarization (VV) and cross-polarization (VH)) acquired by the RADARSAT-2 and Sentinel-1 SAR satellites. Both the WCM and SVM models performed well in estimating the LAI of corn. For the SVM, the correlation (R) between estimated LAI for corn and LAI measured in situ was reported as 0.93, with a root mean square error (RMSE) of 0.64 m2m−2 and mean absolute error (MAE) of 0.51 m2m−2 . The WCM produced an R-value of 0.89, with only slightly higher errors (RMSE of 0.75 m2m−2 and MAE of 0.61 m2m−2 ) when estimating corn LAI. For rice, only the SVM model was tested, given the lack of soil moisture measures for this crop. In this case, both high correlations and low errors were observed in estimating the LAI of rice using SVM (R of 0.96, RMSE of 0.41 m2m−2 and MAE of 0.30 m2m−2 ). However, the results demonstrated that when the calibration points were limited (in this case for soybeans), the WCM outperformed the SVM model. This study demonstrates the importance of testing different modeling approaches over diverse agro-ecosystems to increase confidence in model performance.eng
dc.formatapplication/pdfes_AR
dc.language.isoenges_AR
dc.publisherMDPIes_AR
dc.rightsinfo:eu-repo/semantics/openAccesses_AR
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/
dc.sourceRemote Sensing 13 (7) : 1348. (2021)es_AR
dc.subjectLeaf Area Indexeng
dc.subjectÍndice de Superficie Foliares_AR
dc.subjectTeledetección
dc.subjectRemote Sensingeng
dc.subject.otherRADARSAT-2es_AR
dc.subject.otherSentinel-1es_AR
dc.subject.otherWater Cloud Modeleng
dc.subject.otherModelo de Nube de Aguaes_AR
dc.subject.otherMachine Learningeng
dc.subject.otherAprendizaje Automáticoes_AR
dc.titleA comparison between support vector machine and water Cloud model for estimating crop leaf area indexes_AR
dc.typeinfo:ar-repo/semantics/artículoes_AR
dc.typeinfo:eu-repo/semantics/articlees_AR
dc.typeinfo:eu-repo/semantics/publishedVersiones_AR
dc.rights.licenseCreative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.description.filFil: Hosseini, Mehdi. Carleton University. Department of Geography and Environmental Studies; Canadá. University of Maryland. Department of Geographical Sciences; Estados Unidoses_AR
dc.description.filFil: McNairn, Heather. Carleton University. Department of Geography and Environmental Studies; Canada. Agriculture and Agri-Food Canada. Science and Technology Branch; Canadáes_AR
dc.description.filFil: Mitchell, Scott. Carleton University. Department of Geography and Environmental Studies; Canadá.es_AR
dc.description.filFil: Dingle Robertson, Laura. University of Maryland. Department of Geographical Sciences; Estados Unidoses_AR
dc.description.filFil: Davidson, Andrew. Carleton University. Department of Geography and Environmental Studies; Canadá. University of Maryland. Department of Geographical Sciences; Estados Unidoses_AR
dc.description.filFil: Ahmadian, Nima. Julius-Maximilians-Universität; Alemaniaes_AR
dc.description.filFil: Bhattacharya, Avik. Indian Institute of Technology. Centre of Studies in Resources Engineering. Microwave Remote Sensing Lab; Indiaes_AR
dc.description.filFil: Borg, Erik. German Aerospace Center. Department of National Ground Segment; Alemaniaes_AR
dc.description.filFil: Conrad, Christopher. University of Halle-Wittenberg. Institute of Geosciences and Geography; Alemaniaes_AR
dc.description.filFil: Dabrowska-Zielinska, Katarzyna. Institute of Geodesy and Cartography; Poloniaes_AR
dc.description.filFil: De Abelleyra, Diego. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; Argentinaes_AR
dc.description.filFil: Gurdak, Radoslaw. Institute of Geodesy and Cartography; Poloniaes_AR
dc.description.filFil: Kumar, Vineet. Indian Institute of Technology. Centre of Studies in Resources Engineering. Microwave Remote Sensing Lab; India. Delft University of Technology. Department of Water Management; Países Bajoses_AR
dc.description.filFil: Kussul, Nataliia. Space Research Institute of National Academy of Sciences of Ukraine and State Space Agency of Ukraine; Ucraniaes_AR
dc.description.filFil: Mandal, Dipankar. Indian Institute of Technology. Centre of Studies in Resources Engineering. Microwave Remote Sensing Lab; Indiaes_AR
dc.description.filFil: Rao, Y. S. Indian Institute of Technology. Centre of Studies in Resources Engineering. Microwave Remote Sensing Lab; Indiaes_AR
dc.description.filFil: Saliendra, Nicanor. USDA-ARS Northern Great Plains Research Laboratory; Estados Unidoses_AR
dc.description.filFil: Shelestov, Andrii. Space Research Institute of National Academy of Sciences of Ukraine and State Space Agency of Ukraine; Ucraniaes_AR
dc.description.filFil: Spengler, Daniel. Deutsches GeoForschungs Zentrum (GFZ). Division of Remote Sensing; Alemaniaes_AR
dc.description.filFil: Verón, Santiago Ramón. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Métodos Cuantitativos y Sistemas de Información; Argentinaes_AR
dc.description.filFil: Homayouni, Saeid. Institut National de la Recherche Scientifique (INRS). Center Eau Terre Environnement; Canadáes_AR
dc.description.filFil: Becker-Reshef, Inbal. University of Maryland, Department of Geographical Sciences; Estados Unidoses_AR
dc.subtypecientifico


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