| 光谱学与光谱分析 |
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| Predicting Nitrogen Concentrations from Hyperspectral Reflectance at Leaf and Canopy for Rape |
| WANG Yuan1,2,HUANG Jing-feng1,３*,WANG Fu-min1,2,LIU Zhan-yu1,2 |
1. Institute of Agricultural Remote Sensing and Information Application, Huajiachi Campus, Zhejiang University, Hangzhou 310029, China
2. Ministry of Education Key Laboratory of Environmental Remediation and Ecological, Health, Zhejiang University, Hangzhou310029, China
3. Key Laboratory of Agricultural Remote Sensing and Information System Application, Zhejiang Province, Hangzhou 310029, China |
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Abstract An experiment was designed to determine whether nitrogen concentrations could be predicted from reflectance (R) spectra of rape leaves in laboratory, and, if so, whether the predictive spectral features could be correlated with nitrogen concentration of simple canopies of rape. The best predictors for nitrogen in leaves appeared with first-difference transformations of R, and the bands selected were similar to those found in other studies. Shortwave infrared bands were best predictors for nitrogen. In the shortwave infrared region, however, the absolute differences in reflectance at critical bands were extremely small, and the bands of high correlation were narrow. High spectral and radiance resolution are required to resolve these differences accurately. Variability in canopy reflectance in shortwave infrared region was at least an order of magnitude beyond that necessary to detect signals from chemicals. The variability in first-difference R and log 1/R on canopy scales were related to the arrangement of trees with respect to direct solar radiation, instrument noise, leaf fluttering, and small change in atmospheric moisture. The first-difference of reflectance R based regressions prediction of nitrogen concentration at canopy level gets a good fitness.
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Received: 2006-08-28
Accepted: 2006-11-28
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Corresponding Authors:
HUANG Jing-feng
E-mail: hjf@zju.edu.cn
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[1] Peterson D L，Hubbard G S. Imaging Sci. Technol., 1992, 36: 445.
[2] PENG Yu-kui, LI Ju-ying, QI Zhen-ying(彭玉魁, 李菊英, 祁振英). Triticeae Crops(麦类作物), 1997, 17(2): 33.
[3] BAI Qi-lin, CHEN Shao-jiang, DONG Xiao-ling(白琪林, 陈绍江, 董晓玲). Spectroscopy and Spectral Analysis(光谱学与光谱分析),2004,24(11): 1345.
[4] BAI Qi-lin, CHEN Shao-jiang, DONG Xiao-ling(白琪林, 陈绍江, 董晓玲). Spectroscopy and Spectral Analysis (光谱学与光谱分析),2006,26(2): 271.
[5] ZHANG Hong-jiang, WU Jin-hong, MEI Han-wei, et al(张洪江, 吴金红, 梅捍卫，等). Journal of Plant Genetic Resources(植物遗传资源学报), 2005, 6(1): 91.
[6] Yoder B J, Daley L S. Spectroscopy, 1989, 5(8): 44.
[7] Stjacque C, Bellefleur P. Tree Physiol.，1991, 8: 391.
[8] Gausman H W. Remote Sens. Environ., 1977, 6: 1.
[9] Peterson D L, Abet J D, Matson P A. Remote Sens. Environ., 1988,24: 85.
[10] Tang Y L, Wang R C, Huang J F. Pedosphere，2004, 14(4): 467.
[11] Abou-ismail O, Huang J F, Wang R C. Pedosphere，2004, 14(4): 519.
[12] YANG Xiao-hua, HUANG Jing-feng, WANG Fu-min, et al, Journal of Zhejiang University Science A,2006, 7(12): 2002.
[13] WANG Xiu-zhen, HUANG Jing-feng, LI Yun-mei, et al(王秀珍, 黄敬峰, 李云梅, 等). Journal of Remote Sensing(遥感学报),2004, 8(1): 81.
[14] Huang J F, Wang F M, Wang X Z, et al. Communications in Soil Science and Plant Analysis,2004, 35(19&20): 2689.
[15] Wessman C A, Aber J D, Peterson D L, et al. Can. J. For. Res., 1988, 18: 6.
[16] Johnson L, Hlavka C A, Peterson D L. Remote Sens. Environ., 1994, 47: 216.
[17] ZHENG Yong-mei, ZHANG Jun, CHEN Xing-dan, et al(郑咏梅, 张 军, 陈星旦, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析),2004,24(6): 675. |
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