A comparison of two adaptive multivariate analysis methods (PLSR and ANN) for winter wheat yield forecasting using Landsat-8 OLI images

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• 作者：Pengfei Chen^a ; ^c ; ^{pengfeichen-001@hotmail.com} ; Qi Jing^b
• 关键词：Winter wheat ; Yield forecast ; Partial least squares regression ; Artificial neural networks
• 刊名：Advances in Space Research
• 出版年：2017
• 出版时间：15 February 2017
• 年：2017
• 卷：59
• 期：4
• 页码：987-995
• 全文大小：717 K
• 卷排序：59

文摘

An 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 OLI 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 R2, 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 R2, 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.