Scientia Agricultura Sinica ›› 2014, Vol. 47 ›› Issue (19): 3780-3790.doi: 10.3864/j.issn.0578-1752.2014.19.006

• TILLAGE & CULTIVATION·PHYSIOLOGY & ECOLOGY • Previous Articles     Next Articles

Remote Sensing Prediction of Winter Wheat Protein Content Based on Nitrogen Translocation and GRA-PLS Method

LI Zhen-hai1,2,3, XU Xin-gang1,2, JIN Xiu-liang1,2,4, ZHANG Jing-cheng1,2, SONG Xiao-yu1,2SONG Sen-nan1,2,4, YANG Gui-jun1,2, WANG Ji-hua1,2   

  1. 1 Beijing Research Center for Information Technology in Agriculture, Beijing 100097
    2 National Engineering Research Center for Information Technology in Agriculture, Beijing 100097
    3 Institute of Applied Remote Sensing & Information Technology, Zhejiang University, Hangzhou 310029
    4 Key Laboratory of Crop Genetics and Physiology of Jiangsu Province, Yangzhou University, Yangzhou 225009, Jiangs
  • Received:2013-11-20 Revised:2014-05-12 Online:2014-10-01 Published:2014-10-01

Abstract: 【Objective】Prediction of grain protein content (GPC) can provide effective decision-making supporting information for acquisition and processing of high quality wheat. The objective of the study is to demonstrate the feasibility of remote sensing monitoring of wheat grain protein content based on nitrogen translocation theory, and its expansibility between regional and annual level. 【Method】Field experiments of four winter wheat cultivars by four nitrogen applications in Beijing during 2012-2013 growing seasons were carried out for model building. Firstly, the two main sources of grain nitrogen accumulation and their relationships were analyzed based on nitrogen translocation theory and agronomy parameters modeling. The nitrogen remobilization from vegetative organs to grain was considered as the key point, while the nitrogen uptake from the root absorption during grain filling stage was simply calculated as the nitrogen remobilization from vegetative organs to grain multiplied by a factor. Mechanism of predicting GPC with leaf nitrogen content (LNC) at the flowering stage was clarified through integrating agronomy parameters modeling. Meanwhile, the temperature factor was considered. Secondly, twenty-four vegetative indices were selected according to the good relationship between vegetative indices and leaf nitrogen content, and remote sensing estimating of LNC was established by using grey relational method and partial least squares method (GRA-PLS). Therefore, a prediction model of GPC with remote sensing was established. 【Result】The results showed that the selected five vegetative indices according to grey relational grade were mND705, NDVIcanste, Readone, DCNI and NDCI. For the LNC estimating, the determination coefficient (R2) and corresponding to root mean square error (RMSE) of modeling and validation results were 0.859, 0.257% and 0.726, 0.063%, respectively. Estimation of LNC has good robustness by using GRA-PLS method. The R2 and RMSE of predicted and measured GPC of modeling and validation results were 0.726, 1.30% and 0.609, 1.19%, respectively. The results indicated that it was available to estimate GPC by integration model of nitrogen translocation theory and GRA-PLS method. 【Conclusion】The integration model with explanatory and expansibility could explain the theory of “why the LNC is used to predict GPC”, achieved prediction of grain protein content between regional and annual levels, and had a wide range of potential applications.

Key words: grain protein content, nitrogen translocation, grey relational method; partial least squares method, vegetation index

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