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Predicting grain yield and protein content in wheat by fusing multi-sensor and multi-temporal remote-sensing images

文献类型： 外文期刊

作者： Wang, Laigang 1 ; Tian, Yongchao 1 ; Yao, Xia 1 ; Zhu, Yan 1 ; Cao, Weixing 1 ;

作者机构： 1.Nanjing Agr Univ, Jiangsu Key Lab Informat Agr, Natl Engn & Technol Ctr Informat Agr, Nanjing 210095, Jiangsu, Peoples R China

2.Henan Acad Agr Sci, Inst Agr Econ & Informat, Zhengzhou 410002, Henan, Peoples R China

关键词： Wheat;Grain yield;Protein content;Multi-sensor RS;Multi-temporal RS;RS data fusion

期刊名称：FIELD CROPS RESEARCH （ 影响因子：5.224； 五年影响因子：6.19 ）

ISSN：

年卷期：

页码：

收录情况： SCI

摘要： Non-destructive and quick assessment of grain yield and protein content is needed in modern wheat production. This study was undertaken to determine the optimal spectral index and the best time for predicting grain yield and grain protein content in wheat by fusing multi-sensor and multi-temporal remote-sensing images. Four field experiments were carried out at different locations, cultivars and nitrogen rates in two growing seasons of winter wheat (Triticum aestivum L.). During the experiment periods, data were obtained on time series RS images fused with high temporal and spatial resolutions, along with grain yields and protein contents at maturity. The results showed that the normalized difference vegetation index (NDVI) estimated by fusion exhibits high consistency with the SPOT-5 NDVI, which confirmed the usefulness of related algorithm. The periods around initial gain filling and anthesis stages were identified as the best periods for estimating wheat grain yield and protein content, respectively. The use of ratio vegetation index (RVI) (Nir, Red) at the initial filling stage obtained enhanced accuracy in wheat yield prediction, while the index R-Nir/R-Red +R-Green) during anthesis predicted grain protein content more accurately than that at other growth stages. In addition, the accumulated spectral index Sigma RVI (Nir, Red) and Sigma(R-Nir/(R-Red + R-Green)) from jointing to initial filling stage gave higher prediction accuracy for grain yield and protein content, respectively, than the spectral index at a single period. These results help provide a technical approach to the prediction of grain yield and grain protein content in wheat with remote sensing at a large scale. (C) 2014 Elsevier B.V. All rights reserved.

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