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
文献类型：期刊论文
| 作者 | Feng Shuai; Xu Tongyu; Yu Fenghua; Chen Chunling; Yang Xue; Wang Nianyi |
| 刊名 | SPECTROSCOPY AND SPECTRAL ANALYSIS
 |
| 出版日期 | 2019 |
| 卷号 | 39期号:10页码:3281-3287 |
| 关键词 | Rice Nitrogen Unmanned aerial vehicle Hyperspectral processing Vegetation index Inversion model |
| ISSN号 | 1000-0593 |
| 其他题名 | 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 |
| 英文摘要 | 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 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. |
| 语种 | 英语 |
| CSCD记录号 | CSCD:6590786 |
| 源URL | [http://ir.imr.ac.cn/handle/321006/147823]  |
| 专题 | 金属研究所_中国科学院金属研究所 |
| 作者单位 | 中国科学院金属研究所 |
| 推荐引用方式 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. |
入库方式： OAI收割
来源：金属研究所
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