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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
Alternative TitleResearch 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 Shuai; Xu Tongyu; Yu Fenghua; Chen Chunling; Yang Xue; Wang Nianyi
2019
Source PublicationSPECTROSCOPY AND SPECTRAL ANALYSIS
ISSN1000-0593
Volume39Issue:10Pages:3281-3287
AbstractIn 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 R 2 at the tillering, jointing and heading stages were; DSI(R-857, R-623), 0. 704; DSI(R-670, R-578), 0. 786; DSI(R-995, R-508), 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 R-2 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.
KeywordRice Nitrogen Unmanned aerial vehicle Hyperspectral processing Vegetation index Inversion model
Indexed ByCSCD
Language英语
CSCD IDCSCD:6590786
Citation statistics
Document Type期刊论文
Identifierhttp://ir.imr.ac.cn/handle/321006/147824
Collection中国科学院金属研究所
Affiliation中国科学院金属研究所
Recommended Citation
GB/T 7714
Feng Shuai,Xu Tongyu,Yu Fenghua,et al. 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[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS,2019,39(10):3281-3287.
APA Feng Shuai,Xu Tongyu,Yu Fenghua,Chen Chunling,Yang Xue,&Wang Nianyi.(2019).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,39(10),3281-3287.
MLA Feng Shuai,et al."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 39.10(2019):3281-3287.
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