Estimating biophysical parameters of rice with remote sensing data using support vector machines

Yang, XH; Huang, JF; Wu, YP; Wang, JW; Wang, P; Wang, XM; Huete, AR

Estimating biophysical parameters of rice with remote sensing data using support vector machines

Yang, XH Huang, JF Wu, YP Wang, JW Wang, P Wang, XM Huete, AR

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Publication Type:: Journal Article
Citation:: Science China Life Sciences, 2011, 54 (3), pp. 272 - 281
Issue Date:: 2011-03-01

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Field	Value	Language
dc.contributor.author	Yang, XH	en_US
dc.contributor.author	Huang, JF	en_US
dc.contributor.author	Wu, YP	en_US
dc.contributor.author	Wang, JW	en_US
dc.contributor.author	Wang, P	en_US
dc.contributor.author	Wang, XM	en_US
dc.contributor.author	Huete, AR https://orcid.org/0000-0003-2809-2376	en_US
dc.date.available	2009-09-08	en_US
dc.date.issued	2011-03-01	en_US
dc.identifier.citation	Science China Life Sciences, 2011, 54 (3), pp. 272 - 281	en_US
dc.identifier.issn	1674-7305	en_US
dc.identifier.uri	http://hdl.handle.net/10453/22147
dc.description.abstract	Hyperspectral reflectance (350-2500 nm) measurements were made over two experimental rice fields containing two cultivars treated with three levels of nitrogen application. Four different transformations of the reflectance data were analyzed for their capability to predict rice biophysical parameters, comprising leaf area index (LAI; m2 green leaf area m-2 soil) and green leaf chlorophyll density (GLCD; mg chlorophyll m-2 soil), using stepwise multiple regression (SMR) models and support vector machines (SVMs). Four transformations of the rice canopy data were made, comprising reflectances (R), first-order derivative reflectances (D1), second-order derivative reflectances (D2), and logarithm transformation of reflectances (LOG). The polynomial kernel (POLY) of the SVM using R was the best model to predict rice LAI, with a root mean square error (RMSE) of 1.0496 LAI units. The analysis of variance kernel of SVM using LOG was the best model to predict rice GLCD, with an RMSE of 523.0741 mg m-2. The SVM approach was not only superior to SMR models for predicting the rice biophysical parameters, but also provided a useful exploratory and predictive tool for analyzing different transformations of reflectance data. © 2011 The Author(s).	en_US
dc.relation.ispartof	Science China Life Sciences	en_US
dc.relation.isbasedon	10.1007/s11427-011-4135-4	en_US
dc.subject.classification	Plant Biology & Botany	en_US
dc.subject.mesh	Oryza sativa	en_US
dc.subject.mesh	Plant Leaves	en_US
dc.subject.mesh	Crops, Agricultural	en_US
dc.subject.mesh	Nitrogen	en_US
dc.subject.mesh	Chlorophyll	en_US
dc.subject.mesh	Telemetry	en_US
dc.subject.mesh	Spectrum Analysis	en_US
dc.subject.mesh	Decision Making	en_US
dc.subject.mesh	Biophysics	en_US
dc.subject.mesh	Algorithms	en_US
dc.subject.mesh	Models, Theoretical	en_US
dc.subject.mesh	Agriculture	en_US
dc.subject.mesh	Oryza	en_US
dc.title	Estimating biophysical parameters of rice with remote sensing data using support vector machines	en_US
dc.type	Journal Article
utslib.citation.volume	3	en_US
utslib.citation.volume	54	en_US
utslib.for	0703 Crop and Pasture Production	en_US
utslib.for	0607 Plant Biology	en_US
utslib.for	06 Biological Sciences	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Life Sciences
pubs.organisational-group	/University of Technology Sydney/Strength - CAMGIS - Centre for Advanced Modelling and Geospatial lnformation Systems
utslib.copyright.status	closed_access
pubs.issue	3	en_US
pubs.publication-status	Published	en_US
pubs.volume	54	en_US

Abstract:

Hyperspectral reflectance (350-2500 nm) measurements were made over two experimental rice fields containing two cultivars treated with three levels of nitrogen application. Four different transformations of the reflectance data were analyzed for their capability to predict rice biophysical parameters, comprising leaf area index (LAI; m2 green leaf area m-2 soil) and green leaf chlorophyll density (GLCD; mg chlorophyll m-2 soil), using stepwise multiple regression (SMR) models and support vector machines (SVMs). Four transformations of the rice canopy data were made, comprising reflectances (R), first-order derivative reflectances (D1), second-order derivative reflectances (D2), and logarithm transformation of reflectances (LOG). The polynomial kernel (POLY) of the SVM using R was the best model to predict rice LAI, with a root mean square error (RMSE) of 1.0496 LAI units. The analysis of variance kernel of SVM using LOG was the best model to predict rice GLCD, with an RMSE of 523.0741 mg m-2. The SVM approach was not only superior to SMR models for predicting the rice biophysical parameters, but also provided a useful exploratory and predictive tool for analyzing different transformations of reflectance data. © 2011 The Author(s).

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http://hdl.handle.net/10453/22147