Abstract：It is one of the necessary measures to achieve accurate regulation and scientific management of rice production using the characteristic bands of hyperspectral reflectance curve to construct spectral index and establish chlorophyll content inversion model.In order to establish a hyperspectral inversion model for relative chlorophyll content (SPAD values) of rice leaves at the jointing and booting stage, the hyperspectral data and SPAD values of rice leaves at the jointing and booting stage were obtained respectively. The original spectral reflectance curve was denoised utilizing using the wavelet analysis method, and the spectral index NAOC based on the integral operation was simplified to obtain a simple spectral reflectance curve based on dual-wavelength. The correlation coefficients between SPAD values of rice leaves at jointing and booting stage and the optimized spectral and transformed spectral indices constructed by the original reflectance spectrum R and mathematical transformation spectrum LgR, 1/R and R were calculated by the correlation analysis method. The two-dimensional matrix of correlation coefficients with the integration limit (a, b) as the abscissa and ordinate was obtained. Three band combinations with the highest correlation coefficient: R (641, 790) (0.872 6), R(653, 747) (0.871 7) and R (644, 774) (0.871 6) were selected to calculate 60 optimized spectral indices corresponding to the combination of three integral bands in 20 original samples, which were divided into modeling set and validation set according to the ratio of 2∶1. Three SPAD inversion models of rice leaves were established: partial least squares regression model (PLSR), support vector machine (SVM) and BP neural network. The results showed that: the determination coefficients of the three SPAD inversion models were all greater than 0.79, and the normalizedroot mean square error was less than 5.4%. Compared with the other two models, BP neural network has the highest fitting degree and the highest prediction accuracy, the modeling set R2=0.842 6, NRMSE=5.152 7%; the verification set R2=0.857, NRMSE=4.829 9%. In general, it is feasible to establish an SPAD inversion model of rice leaves at the jointing and booting stage based on optimized spectrum and transformed spectrum index after simplified operation of dual-wavelength. The results of SPAD inversion of rice leaves by BP neural network are ideal and better than the other two inversion models, which have a certain reference value for improving the precision control technology of rice at jointing and booting stage establishing a scientific management system for rice production.
Key words：Rice; SPAD; Optimized spectral index; Hyperspectral; BP neural network
于 跃，于海业，李晓凯，王洪健，刘 爽，张 蕾，隋媛媛. 优化光谱指数建立水稻叶片SPAD的高光谱反演模型[J]. 光谱学与光谱分析, 2022, 42(04): 1092-1097.
YU Yue, YU Hai-ye, LI Xiao-kai, WANG Hong-jian, LIU Shuang, ZHANG Lei, SUI Yuan-yuan. Hyperspectral Inversion Model for SPAD of Rice Leaves Based on Optimized Spectral Index. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(04): 1092-1097.
[1] PEI Xin-biao, WU He-long, MA Ping, et al（裴信彪，吴和龙，马 萍，等）. Chinese Optics（中国光学）, 2018, 11(5): 835.
[2] Cui Shichao, Ding Rufu, Zhou Kefa. Journal of Plant Growth Regulation, 2019, 38(3)：956.
[3] LUO Dan, CHANG Qing-rui, QI Yan-bing, et al（罗 丹，常庆瑞，齐雁冰，等）. Journal of Triticeae Crops（麦类作物学报）, 2016, 36(9): 1225.
[4] LIU Tan, XU Tong-yu, YU Feng-hua, et al（刘 潭，许童羽，于丰华，等）. Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2020, 51(5): 156.
[5] Bikal Ghimire, Deepika Timsina, Jyotsna Nepal. Journal of Maize Research and Development, 2015, 1(1): 134.
[6] Nieves Pasqualotto, Guido D’Urso, Salvatore Falanga Bolognesi, et al. Agronomy, 2019, 9(10): 663.
[7] Wu Chuanyu, Liu Meiling, Liu Xiangnan, et al. International Journal of Environmental Research and Public Health, 2019, 16(23)：114.
[8] LI Xu-qing, LI Long, ZHUANG Lian-ying, et al（李旭青，李 龙，庄连英，等）. Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2019, 50(6): 226.
[9] Qi Xiaotong, Jiang Jinbao, Cui Ximin, et al. Food Analytical Methods, 2020, 13(2): 1.
[10] Facundo Carmona, Raúl Rivas, Diana C, et al. European Journal of Remote Sensing, 2015, 48(1): 319.
[11] CAI Liang-hong, DING Jian-li（蔡亮红，丁建丽）. Transactions of the Chinese Society of Agricultural Engineering（农业工程学报）, 2017, 33(16): 144.
[12] LI Bao, WANG Meng-he, WANG Guang-sheng, et al（李 宝，王孟和，汪光胜，等）. Engineering of Surveying and Mapping（测绘工程）, 2018, 27(10): 1.
[13] Li Hongjun, Zhang Yuming, Lei Yuping, et al. Remote Sensing, 2019, 12(1): 95.
[14] Huete A R. Remote Sensing of Environment, 1988, 25: 295.
