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作者	单位
张秋阳，杨贵军，徐新刚，李振海，张长利，王树文	（1.东北农业大学，黑龙江哈尔滨 150030； 2.北京农业信息技术研究中心/国家农业信息化工程技术研究中心，北京 100097； 3.农业部农业信息技术重点实验室，北京 100097）

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中文摘要:

为给大区域范围的冬小麦氮素营养遥感诊断及其精准施肥决策提供参考，将GeoEye 1高分辨率遥感影像数据与氮肥优化算法（NFOA）相结合，开展了冬小麦氮肥推荐应用研究。首先，基于多年地面实测冬小麦冠层高光谱数据，利用光谱响应函数生成GeoEye 1卫星遥感模拟数据，计算得到归一化植被指数NDVI，并结合当季估产指数INSEY构建了冬小麦潜在产量预测模型；通过定义可表征小麦氮素丰缺的氮素响应指数RI_NDVI，结合潜在产量模型，计算得到氮素需求量；最后，利用GeoEye 1高分辨率遥感影像数据进行验证分析，将氮素推荐模型与高分辨率遥感数据相结合生成施肥推荐处方图，实现了冬小麦的氮素营养诊断及施肥推荐。结果表明，当季估产指数INSEY可很好地估算冬小麦的潜在产量（r= 0.606，RMSE=0.704 t·hm^-2），基于GeoEye 1高分遥感影像提取NDVI预测的潜在产量与实测产量显著相关（r= 0.722，RMSE= 0.451 t·hm^-2）。氮素响应指数RI_NDVI与氮营养指数NNI的倒数也显著相关（r=0.915），可以用RI_NDVI来诊断冬小麦氮素的丰缺状态。以上结果说明，在没有地面实测小麦氮含量、生物量、地面光谱等数据的情况下，利用高分辨率遥感数据与气象数据构建模型可估算冬小麦的潜在产量，并能实现对冬小麦的氮营养诊断及生成推荐施肥处方。

英文摘要:

It is most significant for maintaining crop yields, improving N use efficiency (NUE) and reducing environmental pollution to estimate large scale winter wheat N status in time and to make precision recommendations on the N rate using high resolution remote sensing image. This study attempted to combine high resolution remote sensing image, GeoEye 1 date, with nitrogen fertilizer optimization algorithm (NFOA), to develop its application on N rate recommendation for winter wheat in Beijing area. Firstly, based on the winter wheat canopy hyperspectral data which were actually measured under laboratory condition, according to the spectral response functions, the simulated data of GeoEye 1 was generated. The normalized difference vegetation index (NDVI) was calculated by simulated data and was used to estimate winter wheat yield potential through combining meteorological data and actual yields. Then the topdressing N requirement was calculated combining potential yield with the responsiveness to applied nitrogen (RI_NDVI), which could represent winter wheat N status. Finally, the results were validated by GeoEye 1satellite observation. A prescription map of variable rate fertilization could be generated combining the model with high resolution image and diagnosis of N status, and the recommendation for N rate was provided. The results showed that the in season estimated yield (INSEY) was exponentially related with actual yields and could be used to estimate winter wheat yield potential (r= 0.606，RMSE= 0.704 t·hm^-2). The potential yield which was predicted based on NDVI from GeoEye 1 high resolution remote sensing image was linearly related with the actual yield (r= 0.722，RMSE= 0.451 t·hm^-2). There was a significant relationship between nitrogen nutrition index（NNI）with RI_NDVI (r=0.915). RI_NDVI could be an effective indicator in diagnosing winter wheat N status. It was demonstrated in this study that the winter wheat yield potential could be estimated without nitrogen content, biomass and ground canopy spectral, using the model and combining with high resolution remote sensing image and meteorological data. It can also conduct diagnosis of nitrogen nutrition, recommend N application prescription and provide a meaningful reference for precision variable rate fertilization in the field.

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