中文摘要：

籽粒蛋白含量（grain protein content,GPC）是衡量小麦品质的重要指标,及时准确的预测小麦GPC有利于小麦的分类收割和分级存储。为了能够选择一个合适的氮素营养指标作为中间变量来反演小麦GPC,该文研究分别以开花期植株氮素累积量（plant nitrogen accumulation,PNA）、植株氮素含量（plant nitrogen content,PNC）、叶片氮素累积量（leaf nitrogen accumulation,LNA）和叶片氮素含量（leaf nitrogen content,LNC）4个氮素营养指标为中间变量,并运用支持向量机（support vector machines,SVM）算法实现4个氮素营养指标的估测,最后构建及评价基于开花期“植被指数（vegetation index,VI）-氮素营养指标（nitrogen nutrition index,NNI）-GPC”模式的冬小麦GPC预测模型。结果表明：1）通过分析植被指数与氮素营养指标的相关性,选择植被指数MSAVI、PSRI、DVI、RDVI和GNDVI作为氮素营养指标模型的构建变量;2）运用SVM方法构建的VI-NNI模型中LNC的建模精度与验证精度相对最优,其建模决定系数（coefficient of determination,R2）和验证集标准均方根误差（normalized root mean squared error,n RMSE）及验证标准化平均误差（normalized average error,NAE）分别为0.820、9.553%、-1.4%,验证结果稳定性较好;3）构建NNI-GPC模型中PNC的建模精度与验证精度相对最好,其建模R2和验证n RMSE及NAE分别为0.653、9.843%、-0.3%;4）最终构建的VI-NNI-GPC模型中,以开花期PNC为中间变量的模型建模及反演精度最好,其建模R2和验证n RMSE及NAE分别为0.631、8.564%、-0.9%。以氮素营养指标为中间变量的GPC遥感反演是可行的,并且比较4个氮素营养指标为中间变量反演GPC,PNC具有较高精度的预测结果,为精确反演GPC提供一个可靠的依据,具有一定的应用前景。

英文摘要：

Grain protein content （GPC） is an important quality index for wheat to meet a variety of needs of the commodity. Advanced site-specific knowledge of GPC would provide opportunities to the classification of wheat harvest and graded storage. Areas with higher GPC can be distinguished from the rest to maximize the price premium. Advanced knowledge of grain protein of the wheat may also provide opportunities to manipulate inputs to optimize outputs. In order to select an appropriate nitrogen nutrition index as an intermediate variable to improve the inversion accuracy of wheat GPC, in this study, the GPC predicting models at anthesis with vegetation index （VI） -nitrogen nutrition index （NNI） - GPC pattern were constructed and evaluated. The NNI included 4 nitrogen nutrition index, i.e. plant nitrogen accumulation （PNA）, plant nitrogen content （PNC）, leaf nitrogen accumulation （LNA） and leaf nitrogen content （LNC）. In previous studies, only a single nitrogen nutrition index was used as the intermediate variable to construct GPC model, and it did not indicate which of the parameters could be utilized as the intermediate variable to obtain the best result. To improve the prediction model accuracy of GPC, we chose the optimal intermediate variable to retrieve the GPC of winter wheat in this study. Field experiments of 6 winter wheat cultivars in Beijing during the growing seasons of 2008-2011 and 2012-2015 were carried out for model building. Firstly, suitable vegetation indices were selected through analyzing the correlation between vegetation indices and nitrogen nutrition index to construct the model of VI-NNI by the support vector machines （SVM） algorithm and the optimal one was selected from the 4 nitrogen nutrition index. Secondly, the measured nitrogen nutrition index and winter wheat GPC were used to construct the NNI-GPC model, and another optimal one was obtained from the 4 nitrogen nutrition index. Thirdly, the 4 nitrogen nutrition index would be used as the intermediate variabl