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摘要： 快速、无损、及时地准确估算苹果叶片氮含量是保证苹果产量和质量的基础，利用高光谱技术对苹果叶片氮含量进行遥感反演可为合理施肥提供理论依据。利用2012年和2013年山东省肥城市潮泉镇下寨村不同生育期的苹果叶片氮含量和相应的叶片光谱数据进行分析和建模。首先分析了叶片氮含量与原始光谱、一阶微分及三边光谱指数之间的相关性，筛选出对叶片氮含量敏感的光谱指数；构建了对叶片氮含量敏感的光谱指数NDSI和RSI；最后利用筛选的敏感光谱指数及构建的NDSI和RSI光谱指数，结合灰色关联分析（GRA）-偏最小二乘（PLS）方法及袋外数据重要性（OOB）-随机森林（RF）方法对叶片氮含量进行反演。结果表明：①叶片氮含量与原始光谱、一阶微分光谱之间的敏感波段分别为553、711 、527、708 和559 nm；构建的对叶片氮含量敏感的光谱指数分别为NDSI（567，615）和RSI（554，615）；叶片氮含量对三边光谱指数之间相关性最好的光谱指数是SDy。②建模和验证结果表明用OOB-RF建立的苹果叶片氮含量估算模型具有较好的精度和可靠性，可以用来指导果树变量施肥，为监测氮素营养状况提供一种新的方法。 关键词： 苹果叶片;  叶片氮含量;  灰色关联分析;  随机森林;  偏最小二乘法     Abstract: Estimating nitrogen content of apple leaves rapidly non-destructive and timely is the basis of ensuring apple yield and quality， and the inversion of leaf nitrogen content using hyperspectral technology can provide theoretical basis for reasonable fertilization. The spectral and corresponding leaf nitrogen content of apple leaves were analyzed and modeling in apple critical growing stage from 2012 to 2013 in Feicheng， Shandong Province. Based on the above data， the correlation between leaf nitrogen content and original spectrum， first order differential spectrum， three-sided spectral index was firstly analysed in order to select sensitive spectral index of leaf nitrogen content； Secondly， the spectral index NDSI and RSI was built which were sensitive to leaf nitrogen content； Finally， the prediction model of the apple leaf nitrogen content was established based on the way that was grey correlation analysis-partial least squares regression and out-of-bag data- random forest algorithm. The results showed： （1） The sensitive bands between leaf nitrogen content and original spectrum and first-order differential spectrum were 553， 711， 527， 708 and 559 nm； the spectral indices sensitive to leaf nitrogen content were NDSI（567，615）and RSI（554，615）； the best correlation between leaf nitrogen content and the three-sided spectral index was Sdy. （2） The result showed that OOB-RF estimation model had better accuracy and reliability， which can guide fruit tree variable fertilization using leaf nitrogen content. This way achieved prediction of leaf nitrogen content between regional and annual levels， and had a wide range of potential applications.

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