基于氮素运转原理和GRA-PLS算法的冬小麦籽粒蛋白质含量遥感预测

doi:10.3864/j.issn.0578-1752.2014.19.006

中国农业科学 ›› 2014, Vol. 47 ›› Issue (19): 3780-3790.doi: 10.3864/j.issn.0578-1752.2014.19.006

• 耕作栽培·生理生化·农业信息技术 • 上一篇下一篇

基于氮素运转原理和GRA-PLS算法的冬小麦籽粒蛋白质含量遥感预测

李振海^1,2,3，徐新刚^1,2，金秀良^1,2,4，张竞成^1,2，宋晓宇^1,2，宋森楠^1,2,4，杨贵军^1,2，王纪华^1,2

¹北京农业信息技术研究中心，北京 100097
²国家农业信息化工程技术研究中心，北京 100097
³浙江大学遥感与信息技术应用研究所，杭州 310029
⁴扬州大学江苏省作物遗传生理重点实验室，江苏扬州 225009

收稿日期:2013-11-20 修回日期:2014-05-12 出版日期:2014-10-01 发布日期:2014-10-01
通讯作者: 王纪华，Tel：010-51503488；E-mail：wangjh@nercita.org.cn
作者简介:李振海，Tel：010-51503215；E-mail：zhli323@gmail.com；lizh323@126.com
基金资助:
国家自然科学基金（41171281）
国家科技支撑计划项目（2012BAH29B03，2012BAH29B04）

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

LI Zhen-hai^1,2,3, XU Xin-gang^1,2, JIN Xiu-liang^1,2,4, ZHANG Jing-cheng^1,2, SONG Xiao-yu^1,2, SONG Sen-nan^1,2,4, YANG Gui-jun^1,2, WANG Ji-hua^1,2

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
⁴Key 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

摘要/Abstract

摘要： 【目的】及时、有效地预测籽粒蛋白质含量，能够为优质小麦品种的收购和加工提供科学合理的决策支持信息。本研究从籽粒蛋白质形成的氮素运转规律出发，研究冬小麦籽粒蛋白质遥感预测的可行性及在区域与年际间的扩展性，为高分辨率遥感卫星进行大面积蛋白质预测提供理论依据。【方法】利用2012—2013年4个冬小麦品种×4个氮肥梯度的试验数据和地面高光谱数据进行建模；基于小麦籽粒蛋白质形成的氮素运转机理，通过分析籽粒氮素累积量的两个主要来源及其之间的比例关系，重点抓住开花前的植株氮素累积量再运转这一主要来源，而灌浆期根际的氮素直接吸收则通过其与前者的比例关系来确定，通过相关农学参数模型的耦合，同时加入温度影响因子对籽粒氮素运转的影响，初步阐明了利用开花期小麦叶片氮含量可以预测籽粒蛋白质含量的应用机理；然后选择与叶片氮含量相关的植被指数，利用灰色关联分析-偏最小二乘算法（GRA-PLS）选择与叶片氮含量关联度较高的植被指数并进行小麦叶片氮含量的估算，通过与氮素运转模型的耦合构建了基于氮素运转原理的籽粒蛋白质含量遥感预测模型；最后利用2009—2010年的品种×播期×肥料试验和2012—2013年的其他品种氮肥处理试验进行验证。【结果】（1）通过GRA方法对叶片氮含量和植被指数间的关联度进行计算，选择关联度较大的前5个植被指数进行叶片氮含量建模，其植被指数分别为mND705、NDVIcanste、Readone、DCNI和NDCI；（2）通过PLS方法构建的叶片氮含量模型，建模结果的预测值与实测值的决定系数（R2）和均方根误差（RMSE）分别为0.859和0.257%，验证结果的R2和RMSE分别为0.726和0.063%，利用GRA-PLS方法估算叶片氮素含量具有较好的稳定性；（3）构建的蛋白质预测模型，建模结果和验证结果的预测值与实测值的R2和RMSE分别为0.713、1.30%和0.609、1.19%，预测模型具有较高的精度与可靠性。【结论】基于氮素运转规律构建的小麦籽粒蛋白质含量遥感预测模型，可以作为应用开花期遥感信息来预测籽粒蛋白质含量的机理性解释，初步实现了本研究区域和年际间的籽粒蛋白质含量预测，具有一定的应用前景。

关键词: 籽粒蛋白质含量, 氮素运转, 灰色关联分析, 偏最小二乘, 植被指数

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

李振海，徐新刚，金秀良，张竞成，宋晓宇，宋森楠，杨贵军，王纪华. 基于氮素运转原理和GRA-PLS算法的冬小麦籽粒蛋白质含量遥感预测[J]. 中国农业科学, 2014, 47(19): 3780-3790.

LI Zhen-hai, XU Xin-gang, JIN Xiu-liang, ZHANG Jing-cheng, SONG Xiao-yu, SONG Sen-nan, YANG Gui-jun, WANG Ji-hua. Remote Sensing Prediction of Winter Wheat Protein Content Based on Nitrogen Translocation and GRA-PLS Method[J]. Scientia Agricultura Sinica, 2014, 47(19): 3780-3790.

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基于氮素运转原理和GRA-PLS算法的冬小麦籽粒蛋白质含量遥感预测

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

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