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A comparison of two adaptive multivariate analysis methods (PLSR and ANN) for winter wheat yield forecasting using Landsat-8 OLI images
Chen, Pengfei1,3; Jing, Qi2
2017-02-15
Source PublicationADVANCES IN SPACE RESEARCH
ISSN0273-1177
Volume59Issue:4Pages:987-995
Corresponding AuthorChen, Pengfei(pengfeichen-001@hotmail.com)
AbstractAn assumption that the non-linear method is more reasonable than the linear method when canopy reflectance is used to establish the yield prediction model was proposed and tested in this study. For this purpose, partial least squares regression (PLSR) and artificial neural networks (ANN), represented linear and non-linear analysis method, were applied and compared for wheat yield prediction. Multi-period Landsat-8 OLT images were collected at two different wheat growth stages, and a field campaign was conducted to obtain grain yields at selected sampling sites in 2014. The field data were divided into a calibration database and a testing database. Using calibration data, a cross-validation concept was introduced for the PLSR and ANN model construction to prevent over-fitting. All models were tested using the test data. The ANN yield-prediction model produced R-2, RMSE and RMSE% values of 0.61, 979 kg ha(-1), and 10.38%, respectively, in the testing phase, performing better than the PLSR yield-prediction model, which produced R-2, RMSE, and RMSE% values of 0.39, 1211 kg ha(-1), and 12.84%, respectively. Non-linear method was suggested as a better method for yield prediction. (C) 2016 COSPAR. Published by Elsevier Ltd. All rights reserved.
KeywordWinter wheat Yield forecast Partial least squares regression Artificial neural networks
DOI10.1016/j.asr.2016.11.029
WOS KeywordLEAF-AREA INDEX ; HYPERSPECTRAL VEGETATION INDEXES ; ARTIFICIAL NEURAL-NETWORKS ; REMOTE-SENSING DATA ; REFLECTANCE SPECTRA ; SQUARES REGRESSION ; CITRUS YIELD ; WOFOST MODEL ; CROP MODEL ; CORN YIELD
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[41301378] ; Science and Technology Basic Research Program of China[2014FY210150] ; National Research and Development Plan of China[2016YFD0200600] ; Innovation Project of State Key Laboratory of Resources and Environment Information System of China[088RA901YA] ; Guangdong Province[2013B091500075] ; Chinese Academic of Science[2013B091500075]
Funding OrganizationNational Natural Science Foundation of China ; Science and Technology Basic Research Program of China ; National Research and Development Plan of China ; Innovation Project of State Key Laboratory of Resources and Environment Information System of China ; Guangdong Province ; Chinese Academic of Science
WOS Research AreaAstronomy & Astrophysics ; Geology ; Meteorology & Atmospheric Sciences
WOS SubjectAstronomy & Astrophysics ; Geosciences, Multidisciplinary ; Meteorology & Atmospheric Sciences
WOS IDWOS:000393627500006
PublisherELSEVIER SCI LTD
Citation statistics
Cited Times:5[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/64942
Collection中国科学院地理科学与资源研究所
Corresponding AuthorChen, Pengfei
Affiliation1.Chinese Acad Sci, Inst Geograp Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
2.Agr & Agri Food Canada, Ottawa Res & Dev Ctr, Ottawa, ON K1A 0C6, Canada
3.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Jiangsu, Peoples R China
Recommended Citation
GB/T 7714
Chen, Pengfei,Jing, Qi. A comparison of two adaptive multivariate analysis methods (PLSR and ANN) for winter wheat yield forecasting using Landsat-8 OLI images[J]. ADVANCES IN SPACE RESEARCH,2017,59(4):987-995.
APA Chen, Pengfei,&Jing, Qi.(2017).A comparison of two adaptive multivariate analysis methods (PLSR and ANN) for winter wheat yield forecasting using Landsat-8 OLI images.ADVANCES IN SPACE RESEARCH,59(4),987-995.
MLA Chen, Pengfei,et al."A comparison of two adaptive multivariate analysis methods (PLSR and ANN) for winter wheat yield forecasting using Landsat-8 OLI images".ADVANCES IN SPACE RESEARCH 59.4(2017):987-995.
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