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作者	单位
姚永胜，任妮，李卫国，李伟，马廷淮，张宏，董建宾	（1.南京信息工程大学，江苏南京 210044；2.江苏省农业科学院农业信息研究所，江苏南京 210014；3.江苏大学流体机械工程技术研究中心，江苏镇江 212013；4.江苏大学农业工程学院，江苏镇江 212013）

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中文摘要:

为对大田冬小麦叶片氮素含量（LNC）进行快速、准确及无损监测，通过在江苏省泰州泰兴市、盐城大丰区和南通如皋市布设冬小麦遥感监测大田试验，在获取试验样点冬小麦冠层红光波段反射率（RED_ref）、近红外波段反射率（NIR_ref）和计算的十个光谱指数（RVI、NDVI、DVI、SAVI、OSAVI、MSR、RDVI、EVI2、NLI和SVI）基础上，将12个遥感光谱指标与冬小麦LNC进行相关分析，选出与LNC相关性较好的作为模型输入变量，构建基于BP神经网络的冬小麦LNC估测模型, 并利用GF-6/WFV卫星遥感影像对县域冬小麦LNC的空间分布开展监测。结果表明，12个遥感光谱指标与冬小麦LNC之间存在不同程度的相关性，其中NDVI、RVI、MSR、OSAVI和NLI与冬小麦LNC的相关性较好（相关系数不低于0.65）。将优选的5个遥感光谱指标作为模型输入变量，构建基于BP神经网络的冬小麦LNC估测模型（LNC-BPEM），模型的估测精度r²=0.866，RMSE=0.246%，ARE=12.9%。将冬小麦LNC-BPEM估测模型和GF-6/WFV影像结合对县域冬小麦LNC的空间信息监测，获得了如皋县域冬小麦LNC的空间分布特征，该区域冬小麦LNC范围在0.9%~2.0%（长势正常）的种植面积为29 693.3 hm²，占冬小麦总种植面积的74%。这说明利用GF-6/WFV卫星的多个遥感光谱指标与神经网络结合建模可有效估测县域大田冬小麦叶片氮素含量。

英文摘要:

In order to quickly, accurately, and nondestructively monitor leaf nitrogen content (LNC) of winter wheat in the field, the remote sensing estimation experiment of winter wheat were carried out in Taixing County of Taizhou City, Dafeng District of Yancheng City and Rugao County of Nantong City, Jiangsu Province. Based on the red band reflectance (RED_ref) and near-infrared band reflectance (NIR_ref) of winter wheat canopy and ten spectral indices (RVI, NDVI, DVI, SAVI, OSAVI, MSR, RDVI, EVI2, NLI, and SVI), a correlation analysis was performed between twelve remote sensing spectral indices and winter wheat LNC. The remote sensing spectral indices that showed good correlations with winter wheat LNC were selected as model input variables. Subsequently, the winter wheat LNC estimation model based on BP neural network was constructed, and the spatial distribution of winter wheat LNC in the county was monitored using GF-6/WFV satellite remote sensing images. The results showed that twelve remote sensing spectral indices had different degrees of correlation with winter wheat LNC, among which NDVI, RVI, MSR, OSAVI, and NLI had better correlations with winter wheat LNC (the correlation coefficient is not less than 0.65). The optimized five remote sensing spectral indicators were used as model input variables to construct a winter wheat LNC estimation model (LNC_BPEM) based on BP neural network. The estimation accuracy of the model can be illustrated: r²=0.866, RMSE=0.246%, and ARE=12.9%. By combining the LNC_BPEM estimation model and GF6/WFV image to monitor the LNC spatial information of winter wheat in Rugao county, the spatial distribution characteristics of winter wheat LNC (normal growth) were obtained, the planted area of winter wheat with LNC (normal growth) ranging from 0.9% to 2.0% in the study area was 29 693.3 hm², which accounted for 74% of the total planted area of winter wheat. It is indicated that multiple remote sensing spectral indicators of GF-6/WFV satellite combined with neural network modeling can effectively estimate the leaf nitrogen content of winter wheat in county field.

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