2013年9月
Genetic algorithm-based partial least squares regression for estimating legume content in a grass-legume mixture using field hyperspectral measurements
Grassland Science
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- 巻
- 59
- 号
- 3
- 開始ページ
- 166
- 終了ページ
- 172
- 記述言語
- 英語
- 掲載種別
- 研究論文(学術雑誌)
- DOI
- 10.1111/grs.12026
- 出版者・発行元
- WILEY-BLACKWELL
This study investigated the ability of a field hyperspectral radiometer (400-2350nm) and genetic algorithm-based partial least squares (GA-PLS) regression to estimate legume content in a mixed sown pasture in Hokkaido, Japan. Canopy reflectance data and plant samples were obtained from 50 selected sites in the spring (May) and summer (July) of 2007 (n=100). The predictive accuracy of GA-PLS was compared with that of multiple linear regression (MLR) and of standard full-spectrum PLS (FS-PLS) for the spring and summer datasets. Overall, the highest coefficient of determination (R-2) and the lowest root mean squared error of cross validation (RMSECV) values were obtained in the GA-PLS models for both datasets (R-2=0.72-0.86, RMSECV=4.10-5.73%). Selected hyperspectral wavebands in the GA-PLS models did not perfectly match wavelengths identified previously using MLR, but in most cases, they were within 20nm of previously known wavelength regions.
- リンク情報
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- DOI
- https://doi.org/10.1111/grs.12026
- Web of Science
- https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000323848200006&DestApp=WOS_CPL
- Scopus
- https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84883556324&origin=inward
- Scopus Citedby
- https://www.scopus.com/inward/citedby.uri?partnerID=HzOxMe3b&scp=84883556324&origin=inward
- ID情報
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- DOI : 10.1111/grs.12026
- ISSN : 1744-6961
- eISSN : 1744-697X
- SCOPUS ID : 84883556324
- Web of Science ID : WOS:000323848200006