Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture P. R. China, Beijing Research Center for Information Technology in Agriculture, Beijing, 100097, China.
National Engineering Research Center for Information Technology in Agriculture, Beijing, 100097, China.
Sci Rep. 2018 Jul 3;8(1):10034. doi: 10.1038/s41598-018-28351-8.
Ratio of carbon to nitrogen concentration (C/N) that can illuminate metabolic status of C and N in crop leaves is one valuable indicator for crop nutrient diagnosis. This study explored the feasibility of using spectral slope features from hyperspectral measurements with Branch-and-Bound (BB) algorithm to monitor leaf C/N in wheat and barley. Experimental data from barley in 2010 and wheat in 2012 were collected and used. The analyses prove that leaf C/N is closely related to leaf N concentration (LNC), which implies that it is feasible to apply spectral technology to monitor leaf C/N in that LNC may have been effectivly estimated by hyperspectral measurements. The results also show that many spectral slope features proposed in this study exhibit the significant correlations with leaf C/N. The best slope feature could evaluate changes of leaf C/N well, with R of 0.63 for wheat, 0.68 for barley and 0.65 for both species combined, respectively. using BB algorithm with input of optiaml four slope features can improve the accuracy of leaf C/N estimations with R over 0.81. It is concluded that using the spectral slope new features with BB method appears very promising and potential for remotely monitoring leaf C/N in crops.
碳氮浓度比（C/N）可以反映作物叶片中 C 和 N 的代谢状况，是作物养分诊断的一个有价值的指标。本研究探讨了利用高光谱测量的分支定界（BB）算法的光谱斜率特征来监测小麦和大麦叶片 C/N 的可行性。使用了 2010 年大麦和 2012 年小麦的实验数据。分析证明，叶片 C/N 与叶片氮浓度（LNC）密切相关，这意味着可以应用光谱技术来监测叶片 C/N，因为高光谱测量可以有效地估计 LNC。研究结果还表明，本研究提出的许多光谱斜率特征与叶片 C/N 呈显著相关。最佳斜率特征可以很好地评估叶片 C/N 的变化，小麦的 R 为 0.63，大麦的 R 为 0.68，两种作物的 R 为 0.65。使用 BB 算法输入最佳的四个斜率特征，可以将叶片 C/N 估计的精度提高到 0.81 以上。研究表明，使用光谱斜率新特征和 BB 方法来远程监测作物叶片 C/N 具有很大的潜力和应用前景。
