Research of Method for Inverting Nitrogen Content in Canopy Leaves of Japonica Rice in Northeastern China Based on Hyperspectral Remote Sensing of Unmanned Aerial Vehicle
FENG Shuai1, XU Tong-yu1, 2 *, YU Feng-hua1, 2, CHEN Chun-ling1, 2, YANG Xue1, WANG Nian-yi1
1. College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110161, China
2. Liaoning Agricultural Information Technology Center, Shenyang Agricultural University, Shenyang 110161, China
Abstract:In order to explore a better hyperspectral inversion model for monitoring nitrogen content in rice canopy leaves by remote sensing, based on rice plot experiments, the canopy height spectral data of rice at different growth stages were obtained. Based on the comprehensive comparison of the first derivative (1-Der), standard normal variable transformation (SNV) and SG smoothing method, a spectral processing method (SNV-FDSGF) combining standard normal variable transformation with SG filtering method of first derivative was proposed. The sensitive bands of different growth stages were screened out by non-information variable - competitive adaptive reweighted sampling method (UVE-CARS). Two sensitive bands of each growth period were randomly combined to construct a difference spectrum index DSI (difference spectral index), a ratio spectral index RSI (ratio vegetation index) and a normalized spectrum index NDSI (normalized defference spectral index) with high correlation with nitrogen content in rice leaves. Among them, the optimal vegetation index and determination coefficient R2 at the tillering, jointing and heading stages were: DSI(R857, R623), 0.704; DSI(R670, R578), 0.786; DSI(R995, R508), 0.754. Using the superior three planting indices in each growth period as inputs, the adaptive differential optimization extreme learning machine (SaDE-ELM), radial basis function (RBF-NN) and particle swarm optimization BP neural network (PSO-BPNN) inversion models were constructed respectively. The results showed that SaDE-ELM had the best modeling effect. Compared with RBF-NN and PSO-BPNN, the stability and prediction ability of the model were significantly improved. The determination coefficient R2 of training set and verification set of each growth phase inversion model was above 0.810 and RMSE was below 0.400, which could provide certain theoretical basis for quantitative prediction of nitrogen content in rice canopy leaves.
冯 帅,许童羽,于丰华,陈春玲,杨 雪,王念一. 基于无人机高光谱遥感的东北粳稻冠层叶片氮素含量反演方法研究[J]. 光谱学与光谱分析, 2019, 39(10): 3281-3287.
FENG Shuai, XU Tong-yu, YU Feng-hua, CHEN Chun-ling, YANG Xue, WANG Nian-yi. Research of Method for Inverting Nitrogen Content in Canopy Leaves of Japonica Rice in Northeastern China Based on Hyperspectral Remote Sensing of Unmanned Aerial Vehicle. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(10): 3281-3287.
[1] CHEN Yong-zhe, FU Bo-jie, FENG Xiao-ming(陈永喆, 傅伯杰, 冯晓明). Acta Ecologica Sinica(生态学报), 2017, 37(18): 6240.
[2] QIN Zhan-fei, CHANG Qing-rui, XIE Bao-ni, et al(秦占飞, 常庆瑞, 谢宝妮, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2016, 32(23): 77.
[3] WANG Shu-wen, ZHAO Yue, WANG Li-feng, et al(王树文, 赵 越, 王丽凤, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2016, 32(20): 187.
[4] Tyler J Nigon, David J Mulla, Carl J Rosen, et al. Computers& Electronics in Agriculture, 2015, 112(C): 36.
[5] LI Lan-tao, MA Yi, WEI Quan-quan, et al(李岚涛, 马 驿, 魏全全, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2015, 31(20): 147.
[6] YANG Bao-hua, CHEN Jian-lin, CHEN Lin-hai, et al(杨宝华, 陈建林, 陈林海, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2015, 31(22): 176.
[7] LI Fen-ling, CHANG Qing-rui(李粉玲, 常庆瑞). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2017, 48(7): 174.
[8] Martin L Gnyp, Yuxin Miao,Fei Yuan, et al. Field Crops Research, 2014, 155(155): 42.
[9] Yu F H, Xu T Y, Cao Y L, at al. Int. J. Agric. & Biol. Eng., 2016,9(5): 132.
[10] Wang Wei, Yao Xia, Yao Xinfeng, et al. Field Crops Research, 2012, 129(384): 90.
[11] LI Xu-qing, LIU Xiang-nan, LIU Mei-ling, et al(李旭青, 刘湘南, 刘美玲, 等). Journal of Remote Sensing(遥感学报), 2014, 18(4): 923.
[12] WANG Ren-hong, SONG Xiao-yu, LI Zhen-hai, et al(王仁红, 宋晓宇, 李振海, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2014, 30(19): 191.
[13] Yoshio Inoue, Eiji Sakaiya, Yan Zhu, et al. Remote Sensing of Encironment, 2012, 126(6829): 210.