A hierarchical interannual wheat yield and grain protein prediction model using spectral vegetative indices and meteorological data
文献类型： 外文期刊
作者： Li, Zhenhai 1 ; Taylor, James 3 ; Yang, Hao 1 ; Casa, Raffaele 4 ; Jin, Xiuliang 5 ; Li, Zhenhong 6 ; Song, Xiaoyu 1 ; Ya 1 ;
作者机构： 1.Beijing Res Ctr Informat Technol Agr, Key Lab Quantitat Remote Sensing, Minist Agr & Rural Affairs, Beijing 100097, Peoples R China
2.Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China
3.Univ Montpellier, Irstea, Montpellier SupAgro, UMRITAP, F-34000 Montpellier, France
4.Univ Tuscia, DAFNE, Via San Camillo Lellis, I-01100 Viterbo, Italy
5.Chinese Acad Agr Sci, Inst Crop Sci, Minist Agr & Rural Affairs, Key Lab Crop Physiol & Ecol, Beijing 100081, Peoples R China
6.Newcastle Univ, Sch Engn, Newcastle Upon Tyne NE1 7RU, Tyne & Wear, England
关键词： Yield; Grain protein content; Hierarchical linear modeling; Wheat; Hyperspectral; Meteorological factor; China
期刊名称：FIELD CROPS RESEARCH （ 影响因子：5.224； 五年影响因子：6.19 ）
ISSN： 0378-4290
年卷期： 2020 年 248 卷
页码：
收录情况： SCI
摘要： The use of remote sensing data for predicting wheat yield and quality is becoming a more feasible alternative to destructive and post-harvest laboratory-based test methods. However, most prediction models which make use of remote sensing data are statistical rather than mechanistic, therefore difficult to extend at interannual and regional scales. In this work, an interannual expandable wheat yield and quality predicting model using hierarchical linear modeling (HLM) was developed, integrating hyperspectral and meteorological data. The results showed that the ordinary least squares (OLS) regression for predicting wheat yield and grain protein content (GPC), one key indicator of grain quality, had low stability at the interannual extension. The predictive power for yield by HLM method was higher than OLS, with R-2, RMSEv and nRMSE values of 0.75, 1.10 t/ha, and 20.70 %, respectively. GPC prediction by the HLM method was enhanced when the gluten type was considered, with R-2, RMSEv and nRMSE values of 0.85, 1.02 %, and 6.87 %, respectively. The results of this study confirmed that HLM can be a robust method for improving yield and GPC predicting stability under various growing seasons in winter wheat.
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