Estimation of SPAD value in waterlogged winter wheat based on characteristic indices of hyperspectral and digital image

To explore the optimal monitoring method for soil and plant analyzer development (SPAD) of winter wheat under waterlogging stress based on hyperspectral and digital image techno-logy, the correlations between SPAD of the waterlogged winter wheat and fifteen indices of hyperspectral characteristic and fourteen indices of digital image feature were analyzed under a micro-plot which could be irrigated and drainage separately. Then, the BP neural network models for SPAD estimation were constructed based on the optimal monitoring feature indices. Compared with the normal winter wheat, SPAD and the value of hyperspectral reflectance did not change under short-term waterlogging (less than 7 d), whereas the SPAD was significantly decreased after more than 12 d waterlogging treatment with the value being close to zero at the late stage of growth. The estimation accuracy based on the digital image characteristics of green minus red, excess red index, norma-lized redness index and excess green index showed similar results compared to that using the BP network model based on the characteristics of the corresponding hyperspectral band. The highest为了探索基于高光谱和数字图像技术的受渍冬小麦SPAD最优监测方法,本研究基于排灌可控的微区试验,通过分析常用的15个高光谱特征指数和14个数字图像特征指数与受渍冬小麦叶绿素相对含量(SPAD)的相关关系,构建了基于最优监测特征指数的BP神经网络模型,对受渍冬小麦的SPAD进行估算。结果表明: 与正常小麦相比,短期渍水(≤7 d)对冬小麦的SPAD值和高光谱反射率影响不明显,当渍水时间大于12 d时,SPAD值随着渍水时间的增加急剧降低,在生长后期接近于0;基于数字图像特征指数(绿红差值植被指数、超红指数、红光标准化值和超绿指数)的冬小麦SPAD估算结果,与基于相对应的高光谱波段的估算结果基本一致,估算模型实测值与预测值的