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Estimating Severity Level of Cotton Infected Verticillium Wilt Based on Spectral Indices of TM Image

文献类型： 外文期刊

作者： Chen, Bing 1 ; Wang, Keru 1 ; Li, Shaokun 1 ; Xiao, Chunhua 1 ; Chen, Jianglu 1 ; Jin, Xiulinag 1 ;

作者机构： 1.Shihezi Univ, Key Lab Oasis Ecol Agr Xinjiang Corps, Shihezi 832003, Xinjiang, Peoples R China

2.Chinese Acad Agr Sci, Inst Crop Sci, Beijing 100081, Peoples R China

3.Xinjiang Acad Agr Reclamat Sci, Inst Cotton, Shihezi 832000, Xinjiang, Peoples R China

关键词： Cotton;Verticillium Wilt;Disease Severity;TM Image;Spectral Indices;Estimation Models

期刊名称：SENSOR LETTERS （ 影响因子：0.558； 五年影响因子：0.58 ）

ISSN：

年卷期：

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收录情况： SCI

摘要： In this paper, we present a new method for monitoring severity level (SL) of cotton infected Verticillium wilt. Using eight groups' multi-temporal TM images and field-investigating data in the cotton field infected Verticillium wilt, we analyzed the correlation between spectral indices of TM image and SL of disease, and established the estimation models for SL of cotton disease. The results indicated that the SLs of disease were highly significantly positive correlation with the spectral indices values of B1, B3 and RI, highly significantly negative correlation with B4, OSAVI, MSAVI, TSAVI, SVNSWI, SNSWIa, SNSWIb, SVNI, DNSIa, DNSIb, NDSWIa, NDSWIb, RNSWIa, RNSWIb, DVNI, EVI, TVI, NDGI, SAVI, DVI, NDVI, RVI and PVI, significantly negative correlation with SATVI, and no significantly correlation with B2, B5 and B7. Excluded the model of TSAVI, others models based on thirteen spectral indices which were selected out were all achieved higher estimating precision. Compare with the others the models, the linear models of DVI and DNSIb had the highest decision coefficients (0.836 and 0.820), the lowest root mean square errors (0.606 and 0.506) and lower relative error (0.154 and 0.008), and their the slop and intercept approached 1 and 0. So they were commended as best models to estimate SL of cotton disease by spectral indices of satellite image. This study shows that it is feasible to estimate the SL of cotton disease by the spectral indices of satellite image, quantitatively.

  • 相关文献

[1]Estimating severity level of cotton disease based on spcctral indicse of TM image. Chen Bing,Li Shao-Kun,Wang Ke-Ru,Su Yi,Chen Jiang-Lu,Jin Xiu-Liang,Lv Yin-Liang,Diao Wan-Ying,Li Shao-Kun,Wang Ke-Ru,Chen Bing. 2011

[2]Genome-wide association study discovered candidate genes of Verticillium wilt resistance in upland cotton (Gossypium hirsutum L.). Li, Tinggang,Ma, Xuefeng,Li, Nanyang,Zhou, Lei,Gui, Yuejing,Bao, Yuming,Chen, Jieyin,Dai, Xiaofeng,Liu, Zheng,Han, Huanyong. 2017

[3]Research of Influence Factors on Spectral Recognition for Cotton Leaf Infected by Verticillium wilt. Chen Bing,Wang Fang-yong,Han Huan-yong,Liu Zheng,Chen Bing,Xiao Chun-hua,Zou Nan. 2014

[4]Study on Spectrum Characteristics of Cotton Leaf and Its Estimating with Remote Sensing under Aphid Stress. Chen Bing,Wang Ke-ru,Li Shao-kun,Chen Jiang Lu,Su Yi,Wang Ke-ru,Li Shao-kun,Chen Bing,Jing Xia. 2010

[5]Research of Cotton Canopy Characteristic Information by Hyperspectral Remote Sensing Data. Qi Ya-qin,Lv Xin,Duan Zhen-yu. 2013

[6]Fine mapping the QTL qFS-chr.23, using a CSIL. Wang, Youwu,Zhang, Jingchuan,Kong, Guangchao,Lin, Hai.

[7]Fine mapping of clustered quantitative trait loci for fiber quality on chromosome 7 using a Gossypium barbadense introgressed line. Cao, Zhibin,Zhu, Xiefei,Zhang, Tianzhen,Chen, Hong.

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