Using optimal combination method and in situ hyperspectral measurements to estimate leaf nitrogen concentration in barley
Using optimal combination method and in situ hyperspectral measurements to estimate leaf nitrogen concentration in barley
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DOI：
10.1007/s11119-013-9339-0
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发表时间：
2014-04
影响因子：
6.2
通讯作者：
Xingang Xu;C. Zhao;Ji‐Hua Wang;Jingcheng Zhang;Xiao-yu Song
中科院分区：
文献类型：
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作者：
Xingang Xu;C. Zhao;Ji‐Hua Wang;Jingcheng Zhang;Xiao-yu Song
叶片氮浓度（LNC）是作物氮素状态的良好指标，对诊断养分胁迫和指导田间施氮具有特殊意义。高光谱遥感具有无损检测、快速检测等优点，在作物中lnnc的检测中发挥着独特的作用。大麦，特别是麦芽大麦对N营养的要求非常高，需要及时监测和准确估算大麦叶片中的N浓度。高光谱技术有助于对植物氮素状况进行有效的诊断和动态调控。本研究于2010年7月对内蒙古自治区海拉尔农垦地区38块典型大麦地的冠层反射光谱（350 ~ 1 050 nm）进行了测量，并测量了相应的LNC。选取现有的能很好地评价作物氮素状况的光谱指标，估算大麦的LNC。此外，试验了最优组合（OC）方法提取大麦叶片氮变化的敏感指数和一阶光谱导数波段，并期望建立一些组合模型，以提高LNC估计的准确性。结果表明，选取的NPCI、PRI和DCNI等指标均能较好地描述大麦LNC的动态变化。与使用光谱指数、一阶导数和其他方法（如PCA）的单独模型相比，基于OC的组合模型的性能更好。将5个波段的一阶导数与OC相结合的组合模型对大麦LNC的thr2为0.82，RMSE为0.50。这种与地面测量值的良好相关性表明，高光谱反射率和OC法在评估大麦氮素状况方面具有良好的潜力。
Leaf nitrogen concentration (LNC), a good indicator of nitrogen (N) status in crops, is of special significance to diagnose nutrient stress and guide N fertilization in fields. Due to non-destructive and quick detectability, hyperspectral remote sensing plays a unique role in detecting LNC in crops. Barley, especially malting barley, is very demanding for N nutrition and requires timely monitoring and accurate estimation of N concentration in barley leaves. Hyperspectral techniques can help make effective diagnosis and facilitate dynamic regulation of plant N status. In this study, canopy reflectance spectra (between 350 and 1 050 nm) from 38 typical barley fields were measured as well as the corresponding LNC in Hailar Nongken, China’s Inner Mongolia Autonomous Region in July, 2010. Existing spectral indices that are considered to be good indicators for assessing N status in crops were selected to estimate LNC in barley. In addition, the optimal combination (OC) method was tested to extract the sensitive indices and first-order spectral derivative wavebands that are responsible for variation of leaf N in barley, and expected to develop some combination models for improving the accuracy of LNC estimates. The results showed that most of the selected indices (such as NPCI, PRI and DCNI) could adequately describe the dynamic changes of LNC in barley. The combined models based on OC performed better in comparison with the individual models using either spectral indices or first-order derivatives and the other methods (such as PCA). A combined model that integrated the first-order derivatives from five wavebands with OC performed well withR2of 0.82 and RMSE of 0.50 for LNC in barley. This good correlation with ground measurements indicates that hyperspectral reflectance and the OC method have good potential for assessing N status in barley.