| 光谱学与光谱分析 |
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| Study on Hyperspectral Estimation of Pigment Contents in Leaves of Cotton Under Disease Stress |
| CHEN Bing1, 2, LI Shao-kun1, 2*, WANG Ke-ru1, 2, WANG Fang-yong1, 2,XIAO Chun-hua1,PAN Wen-chao1 |
1. Key Laboratory of Oasis Ecology Agriculture of Xinjiang Corps，Shihezi University, Shihezi 832003, China
2. Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing 100081, China |
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Abstract The spectrum reflectance and pigment contents of cotton leaves infected with Verticillium wilt were measured in cotton disease nursery and field in different growth phases, and severity level of Verticillium wilt was investigated. The correlation between pigment contents of cotton leaves with Verticillium wilt and spectra reflectance, the first derivative of reflectance and spectral characteristic parameters were analyzed respectively. The estimation models about leaves pigment contents of disease cotton were established and tested. The results indicated that the correlations were best significant between chlorophyll a, b and a+b contents of leaves and spectral reflectance in visible wave bands, the first derivative spectrum at the wavelength regions of blue edge, yellow edge and red edge, and all spectral characteristic parameters (excluding red edge swing Dr). The models of transformed chlorophyll absorption in reflectance index (TCAR) and the new model of normalized difference vegetation index (NDVI [702,758]) had best estimated precision, and the relative errors were in average within 1.3%. Given that the model of NDVI [702,758] is very simple and practical, it was commended as a best model to estimate chlorophyll a, b and a+b contents of disease cotton. This study shows that leaves hyperspectra data can be used to estimate the pigment contents of cotton leaves quantitatively. This conclusion has also great practice and application value for monitoring the growth state and disease influence evaluation on cotton by using hyperspectral remote sensing.
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Received: 2009-02-22
Accepted: 2009-05-26
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Corresponding Authors:
LI Shao-kun
E-mail: lishk@mail.caas.net.cn
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[1] Collins W. Photogrammetric Engineering and Remote Sensing，1978，44: 43.
[2] Bisun Datt. Remote Sensing of Environment，1998，66(2): 111.
[3] Hatfield J L. Phytopathology，1990，80: 37.
[4] Filella D, Pen-uelas J. International Journal of Remote Sensing, 1994, 15(7): 1459.
[5] Horler D N H，Dockray M, Barber J. Advanced Space Research, 1983，3: 273.
[6] Blackburn G A. Remote Sensing of Environment, 1998, 66(3): 273.
[7] Broge V H，Mortensen J V. Remote Sensing of Environment，2002，81(1): 45.
[8] YAO Xia, WU Hua-bing, ZHU Yan, et al(姚 霞, 吴华兵, 朱 艳, 等). Cotton Science(棉花学报)，2007，19(4): 267.
[9] Haboudane D, Miller J R, Tremblay N，et al. Remote Sensing of Environment，2002，81: 416.
[10] Haboudane D, Tremblay N, Miller J R, et al. Geoscience and Remote Sensing, 2008，42(2): 423.
[11] WU Chang-shan，XIANG Yue-qin，ZHENG Lan-fen，et al(吴长山, 项月琴, 郑兰芬, 等). Journal of Remote Sensing(遥感学报), 2000，4(3): 228.
[12] HUANG Wen-jiang, WANG Ji-hua, LIU Liang-yun，et al(黄文江，王纪华，刘良云，等). Remote Sensing Technology and Application(遥感技术与运用)，2003, 18(4): 206.
[13] YUAN Jie，WANG Deng-wei，HUANG Chun-yan，et al(袁 杰，王登伟，黄春燕，等). Agricultural Research in the Arid Areas(干旱地区农业研究), 2007，25(3): 79.
[14] Sampson P H，Zarco-Tejada P J，Mohammed G H，et al. Forest Science, 2003，49(3): 381.
[15] CHEN Bing，LI Shao-kun，WANG Ke-ru，et al(陈 兵，李少昆，王克如，等). Scientia Agriculture Sinica(中国农业科学)，2007，40(12)：2709.
[16] Lichtenthaler H K. Methods Enzymol., 1987，148: 350.
[17] TIAN Qing-jiu, MIN Xiang-jun(田庆久，闵祥军). Advanced in Earth Sciences(地球科学进展)，1998，(4): 125.
[18] WANG Xiu-zheng，HUANG Jing-feng，LI Yun-mei，et al(王秀珍，黄敬峰，李云梅, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报)，2003，19(2): 144.
[19] Strachana I B，Patteyb E，Boisvert J B. Remote Sensing of Environment，2002，80: 213.
[20] Xu H R，Ying Y B，Fu X P，et al. Biosystem Engineer，2007，96(4): 447.
[21] CHEN Bing, LI Shao-kun, WANG Ke-ru, et al(陈 兵，李少昆，王克如，等). Cotton Science(棉花学报)，2007，19(1): 57.
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