基于氮素营养指数的冬小麦籽粒蛋白质含量遥感反演

基于遥感实现小麦籽粒蛋白质含量提早估测对农业生产具有重要意义。为提高预测小麦籽粒蛋白质含量的准确度，该研究引入能更好反映作物氮素营养状况的农学参数－氮素营养指数，作为衔接遥感信息与产终籽粒蛋白质含量的桥梁。在田间试验的基础上，探讨氮素营养指数与其他农学参数在诊断籽粒蛋白质含量上的优劣，并基于“遥感参数－氮素营养指数－籽粒蛋白质含量”间关系，利用主成分回归算法构建估测籽粒蛋白质含量的遥感反演模型。结果表明，相比于其他参数，冬小麦旗叶期氮素营养指数能更好的反映产终籽粒蛋白质含量；以氮素营养指数为中间变量，所建遥感反演模型能准确预测小麦籽粒蛋白质含量，模型的预测决定系数为0.48，预测标准误差为0.38%，相对误差为2.32%。

Remote detection of wheat grain protein content using nitrogen nutrition index

Early detection of wheat grain protein content using remote sensing technology is very helpful to optimize field management. An agricultural parameter, Nitrogen Nutrition Index (NNI), was introduced in this study. It can be used as an intermediate variable between remote sensing data and grain protein content, which would enhance accuracy of remote detected wheat grain protein content. Field campaign was conducted to obtain remote sensing data and agricultural parameters at flag growth stage of winter wheat. Using these data, the abilities of nitrogen nutrition index and other agricultural parameters for grain protein content prediction were compared. Then, Principal Component Regression method was used to establish grain protein content prediction model based on relationships between remote sensing data and NNI, and between NNI and grain protein content. The results showed NNI was the best agricultural parameter for grain protein content detection, compared with other agricultural parameters. The established prediction model, using NNI and remote sensing data, can detect wheat grain protein content accurately, with a R2 value of 0.48, a root-mean-square-error (RMSE) value of 0.38%, and a relative error value of 2.32%.