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dc.contributor.author
Hosseini, Mehdi
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McNairn, Heather
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Mitchell, Scott
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Robertson, Laura Dingle
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Davidson, Andrew
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Ahmadian, Nima
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Bhattacharya, Avik
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Borg, Erik
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Conrad, Christopher
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Dabrowska Zielinska, Katarzyna
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de Abelleyra, Diego
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Gurdak, Radoslaw
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Kumar, Vineet
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Kussul, Nataliia
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Mandal, Dipankar
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Rao, Y.S.
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Saliendra, Nicanor
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Shelestov, Andrii
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Spengler, Daniel
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Verón, Santiago Ramón
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Homayouni, Saeid
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Becker Reshef, Inbal
dc.date.available
2023-09-27T17:42:06Z
dc.date.issued
2021-04
dc.identifier.citation
Hosseini, Mehdi; McNairn, Heather; Mitchell, Scott; Robertson, Laura Dingle; Davidson, Andrew; et al.; A comparison between support vector machine and water cloud model for estimating crop leaf area index; Multidisciplinary Digital Publishing Institute; Remote Sensing; 13; 7; 4-2021; 1-20
dc.identifier.uri
http://hdl.handle.net/11336/213317
dc.description.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 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.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Multidisciplinary Digital Publishing Institute
dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by/2.5/ar/
dc.subject
LEAF AREA INDEX
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MACHINE LEARNING
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RADARSAT-2
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SENTINEL-1
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WATER CLOUD MODEL
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Otras Ciencias de la Tierra y relacionadas con el Medio Ambiente
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dc.subject.classification
Ciencias de la Tierra y relacionadas con el Medio Ambiente
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dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
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dc.title
A comparison between support vector machine and water cloud model for estimating crop leaf area index
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2023-09-27T13:54:51Z
dc.identifier.eissn
2072-4292
dc.journal.volume
13
dc.journal.number
7
dc.journal.pagination
1-20
dc.journal.pais
Suiza
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dc.journal.ciudad
Basilea
dc.description.fil
Fil: Hosseini, Mehdi. University of Maryland; Estados Unidos. Carleton University; Canadá
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Fil: McNairn, Heather. Carleton University; Canadá.
Science and Technology Branch, Agriculture and Agri-Food Canada; Canadá
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Fil: Mitchell, Scott. Carleton University; Canadá
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Fil: Robertson, Laura Dingle.
Science and Technology Branch, Agriculture and Agri-Food Canada; Canadá
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Fil: Davidson, Andrew. Carleton University; Canadá.
Science and Technology Branch, Agriculture and Agri-Food Canada; Canadá
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Fil: Ahmadian, Nima. Universität Würzburg; Alemania
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Fil: Bhattacharya, Avik. Indian Institute Of Technology Bombay; India
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Fil: Borg, Erik. German Aerospace Center; Alemania
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Fil: Conrad, Christopher. Leibniz Institut Fur Pflanzenbiochemie (ipb Halle);
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Fil: Dabrowska Zielinska, Katarzyna.
Institute of Geodesy and Cartography; Polonia
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Fil: de Abelleyra, Diego. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación de Recursos Naturales. Instituto de Clima y Agua; Argentina
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Fil: Gurdak, Radoslaw.
Institute of Geodesy and Cartography; Polonia
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Fil: Kumar, Vineet. Indian Institute Of Technology Bombay; India. Delft University of Technology; Países Bajos
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Fil: Kussul, Nataliia. National Academy Of Sciences In Ukraine; Ucrania
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Fil: Mandal, Dipankar. Indian Institute Of Technology Bombay; India
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Fil: Rao, Y.S.. Indian Institute Of Technology Bombay; India
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Fil: Saliendra, Nicanor.
USDA-ARS Northern Great Plains Research Laboratory; Estados Unidos
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Fil: Shelestov, Andrii. National Academy Of Sciences In Ukraine; Ucrania
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Fil: Spengler, Daniel. German Research Centre for Geosciences; Alemania
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Fil: Verón, Santiago Ramón. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación de Recursos Naturales. Instituto de Clima y Agua; Argentina
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Fil: Homayouni, Saeid. Centre Eau Terre Environnement; Canadá
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Fil: Becker Reshef, Inbal. University of Maryland; Estados Unidos
dc.journal.title
Remote Sensing
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.3390/rs13071348
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://www.mdpi.com/2072-4292/13/7/1348
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