Wang Hui-fang, Huo Zhi-guo, Zhou Guang-sheng, Liao Qin-hong, Feng Hai-kuan, Wu Li
Chinese Academy of Meteorological Science, Beijing, 100081, PR China; Beijing Research Center for Information Technology in Agriculture, Beijing 100097, PR China.
Chinese Academy of Meteorological Science, Beijing, 100081, PR China.
Plant Physiol Biochem. 2016 Jan;98:39-45. doi: 10.1016/j.plaphy.2015.10.032. Epub 2015 Nov 10.
Freeze injury, one of the most destructive agricultural disasters caused by climate, has a significant impact on the growth and production of winter wheat. Chlorophyll content is an important indicator of a plant's growth status. In this study, we analyzed the hyperspectral reflectance of normal and freeze-stressed leaves of winter wheat using a spectro-radiometer in a laboratory. The response of the chlorophyll spectra of plants under freeze stress was analyzed to predict the severity of freeze injury. A continuous wavelet transform (CWT) was conducted in conjunction with a correlation analysis, which generated a correlation scalogram that summarized the correlation between the chlorophyll content (SPAD value) and wavelet power at different wavelengths and decomposition scales. A linear regression model was established to relate the SPAD values and wavelet power coefficients. The results indicated that the most sensitive wavelet feature (region E: 553 nm, scale 5, R(2) = 0.8332) was located near the strong pigment absorption bands, and the model based on this feature could estimate the SPAD value with a high coefficient of determination (R(2) = 0.7444, RMSE = 7.359). The data revealed that the chlorophyll content of leaves under different low temperatures treatments could be accurately estimated using CWT. Also, this emerging spectral analytical approach can be applied to other complex datasets, including a broad range of species, and may be adapted to estimate basic leaf biochemical elements, such as nitrogen, cellulose, and lignin.
冻害是由气候引起的最具破坏性的农业灾害之一，对冬小麦的生长和产量有重大影响。叶绿素含量是植物生长状况的重要指标。在本研究中，我们在实验室中使用光谱辐射仪分析了冬小麦正常叶片和冻害胁迫叶片的高光谱反射率。分析了冻害胁迫下植物叶绿素光谱的响应，以预测冻害的严重程度。结合相关性分析进行了连续小波变换（CWT），生成了相关尺度图，总结了叶绿素含量（SPAD值）与不同波长和分解尺度下的小波功率之间的相关性。建立了线性回归模型来关联SPAD值和小波功率系数。结果表明，最敏感的小波特征（区域E：553nm，尺度5，R(2)=0.8332）位于强色素吸收带附近，基于该特征的模型能够以较高的决定系数（R(2)=0.7444，RMSE=7.359）估计SPAD值。数据显示，使用CWT可以准确估计不同低温处理下叶片的叶绿素含量。此外，这种新兴的光谱分析方法可应用于其他复杂数据集，包括广泛的物种，并且可能适用于估计叶片的基本生化元素，如氮、纤维素和木质素。
