| 光谱学与光谱分析 |
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| Monitoring of Winter Wheat Stripe Rust Based on the Spectral Knowledge Base for TM Images |
| ZHANG Jing-cheng1，2，LI Jian-yuan2，3，YANG Gui-jun2，HUANG Wen-jiang2，LUO Ju-hua2，WANG Ji-hua1，2* |
1. Institute of Agriculture Remote Sensing and Information System Application, Zhejiang University, Hangzhou 310029, China
2. National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
3. School of Electronic & Information Engineering, Tongji University, Shanghai 201800, China |
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Abstract In most cases, the reversion model for monitoring the severity degree of stripe rust based on the hyperspectral information can not be directly applied by the satellite images with relatively broad bandwidth, while the airborne hyperspectral images can not be applied for large-scale monitoring either, due to the scale limitation of its data and high cost. For resolving this dilemma, we developed a monitoring method based on PHI images, which relies on the construction of spectral knowledge base of winter wheat stripe rust. Three PHI images corresponding to the winter wheat experimental field that included different severity degree of stripe rust were used as a medium to establish the spectral knowledge base of relationships between disease index (DI) and the simulated reflectance of TM bands by using the empirical reversion model of DI(%) and the relative spectral response (RSR) function of TM-5 sensor. Based on this, we can monitor and identify the winter wheat stripe rust by matching the spectral information of an untested pixel to the spectral knowledge base via Mahalanobis distance or spectral angle mapping (SAM). The precision of monitoring was validated by simulated TM pixels, while the effectiveness of identification was tested by pixels from TM images. The results showed that the method can provide high precision for monitoring and reasonable accuracy for identification in some certain growth stages of winter wheat. Based on the simulated TM pixels, the model performed best in the pustulation period, yielded a coefficient of determination R2=0.93, while the precision of estimates dropped in the milk stage, and performed worst in the jointing stage, which is basically inappropriate for monitoring. Moreover, by using the pixels from TM images, the infected pixels could be identified accurately in pustulation and milk stages, while failed to be identified in jointing stage. For matching algorithms, the Mahalanobis distance method produced a slightly better result than SAM method.
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Received: 2009-06-29
Accepted: 2009-10-02
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Corresponding Authors:
WANG Ji-hua
E-mail: wangjh@nercita.org.cn
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[1] Dereje Hailu, Chemeda Fininsa. Crop Protection, 2007, 26(8): 1209.
[2] Lars Wiik. Crop Protection, 2009, 28(1): 82.
[3] Kim K S, Beresford R M. Agricultural and Forest Meteorology, 2008, 148(8-9): 1374.
[4] CHEN Xian-ming, Maey Moore, Eugene A Milus, et al. Plant Disease, 2002, 86: 39.
[5] Kurt S. Crop Protection, 2002, 21: 495.
[6] Kolmer James A. Current Opinion in Plant Biology, 2005, 8: 441.
[7] JIANG Jin-bao, CHEN Yun-hao, HUANG Wen-jiang(蒋金豹, 陈云浩, 黄文江). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2007, 27(7): 1363.
[8] JIANG Jin-bao, CHEN Yun-hao, HUANG Wen-jiang, et al(蒋金豹, 陈云浩, 黄文江, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2008, 24(1): 35.
[9] LUO Ju-hua, HUANG Wen-jiang, WEI Chao-ling, et al(罗菊花, 黄文江, 韦朝领, 等). Journal of Natural Disasters(自然灾害学报), 2008, 17(6): 115.
[10] HUANG Wen-jiang, David W Lamb, NIU Zheng, et al. Precision Agric., 2007, (8): 187.
[11] BAO Yan-song, WANG Ji-hua, LIU Liang-yun, et al(鲍艳松, 王纪华, 刘良云, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2007, 23(2): 139.
[12] SUN Jie, ZHANG Hong-lei(孙 洁, 张宏磊). Microcomputer Information(微计算机信息), 2008, 21: 225.
[13] ZHANG Bing, SHEN Qian, LI Jun-sheng, et al(张 兵, 申 茜, 李俊生, 等). Journal of Lake Sciences(湖泊科学), 2009, 21(2): 182.
[14] SHAO Hui, WANG Jian-yu, XUE Yong-qi(邵 晖, 王建宇, 薛永祺). Journal of Remote Sensing(遥感学报), 1998, 2(4): 251.
[15] WANG Ji-hua，ZHAO Chun-jiang，HUANG Wen-jiang(王纪华，赵春江，黄文江). Foundation and Application of Agricultrual Quantitative Remote Sensing(农业定量遥感基础与应用). Beijing：Science Press(北京：科学出版社)，2008. 357.
[16] XU Wei-dong, YI Qiu, KUANG Ding-bo(许卫东, 尹 球, 匡定波). Journal of Infrared and Millimeter Waves(红外与毫米波学报), 2005, 24(4): 296.
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