Engineering Research Center of Ecology and Agricultural Use of Wetland, Ministry of Education/College of Agriculture, Yangtze University, Jingzhou 434025, Hubei, China.
Ying Yong Sheng Tai Xue Bao. 2021 Mar;32(3):959-966. doi: 10.13287/j.1001-9332.202103.009.
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 between the measured value and the predicted value was 0.86, while the root mean square error (RMSE) was 3.98. Compared with the BP network models built with the digital image feathers, the accuracy of the models based on the four hyperspectral characteristic indices (carotenoid reflex index, yellow edge amplitude, normalized difference vegetation index and structure insensitive pigment index) for SPAD was significantly improved, with the highest of 0.97 and the lowest RMSE of 1.95. Our results suggest that both hyperspectral and digital image technology could be used to estimate SPAD value of waterlogged winter wheat and that the BP network model based on hyperspectral characteristic indices performed better in the estimation accuracy.
为了探索基于高光谱和数字图像技术的冬小麦渍水下 SPAD 值的最佳监测方法，在可单独灌溉和排水的微小区中，分析了渍水下冬小麦 SPAD 值与十五个高光谱特征指数和十四个数字图像特征指数之间的相关性。然后，基于最优监测特征指数，构建了 SPAD 估算的 BP 神经网络模型。与正常冬小麦相比，短期渍水（少于 7 d）下 SPAD 值和高光谱反射率值没有变化，但 12 d 以上渍水处理后 SPAD 值显著降低，生长后期值接近零。基于绿色减红色、过红指数、归一化红色指数和过绿指数的数字图像特征的估算精度与基于相应高光谱带特征的 BP 网络模型的估算精度相似。实测值与预测值之间的最大 为 0.86，均方根误差（RMSE）为 3.98。与基于数字图像特征建立的 BP 网络模型相比，基于四个高光谱特征指数（类胡萝卜素反射指数、黄边幅度、归一化差值植被指数和结构不敏感色素指数）建立的 SPAD 模型的精度显著提高，最大 为 0.97，最低 RMSE 为 1.95。研究结果表明，高光谱和数字图像技术均可用于估算渍水下冬小麦的 SPAD 值，基于高光谱特征指数的 BP 网络模型在估算精度上表现更好。
