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Acta Agron Sin ›› 2012, Vol. 38 ›› Issue (01): 129-139.doi: 10.3724/SP.J.1006.2012.00129


Monitoring Cotton Field with Suspected Verticillium wilt Using Satellite Remote Sensing with TM Satellite Image as an Example

CHEN Bing1,2,3,WANG Ke-Ru1,2,LI Shao-Kun1,2,*,XIAO Chun-Hua1,2,SU Yi1,TANG Qiang1,CHEN Jiang-Lu1,JIN Xiu-Liang1,LÜ Yin-Liang1,DIAO Wan-Ying1,WANG Kai1   

  1. 1 Key Laboratory Of Oasis Ecology Agriculture Of Xinjiang Corps / Shihezi University, Shihezi 832003, China; 2 Institute Of Crop Sciences, Chinese Academy Of Agricultural Sciences / Key Laboratory Of Crop Physiology And Production Ministry Of Agriculture, Beijing 100081, China; 3 Institute Of Cotton, Xinjiang Academy Of Agricultural Reclamation Sciences, Shihezi 83200, China
  • Received:2011-05-25 Revised:2011-09-13 Online:2012-01-12 Published:2011-11-07
  • Contact: 李少昆, E-mail: lishk@mail.caas.net.cn, Tel: 010-68918891

Abstract: The cotton fields of suspected disease were diagnosed and classed through analyzing and electing best period (single period) in multi-temporal information of TM image, which would provide the technology support to take the active measurements for cotton industry and increase the yield and efficiency of cotton production. Multi-temporal satellite images and data of ground surveying were taken in research areas. On the basis of them, the best wave band and the best period used to recognize cotton fields infected disease were selected and the cotton fields with suspected Verticillium wilt were classified and tested. The results showed that the health and disease cotton fields had obvious difference in the major cotton growth stages, it was feasible to diagnose cotton field with disease through the interpretation symbol of satellite image. In addition, TM4 band of satellite could be regarded as the best wave band to monitor cotton field with disease; the peak boll forming sage (from the last ten-day of July to mid Aug) could be regarded as the best period to monitor disease of cotton with TM image. On the basis of above analyses, using the supervised classification of parallelepiped, the cotton fields with suspected Verticillium wilt were divided into three classes: health, light disease and severe disease cotton fields. The proportions of cotton field infected disease were about 29% and 23% in two years, respectively. The tested results indicated the overall accuracy and kappa coefficient were both over 85% at the crucial growth periods in two years. The further analysis revealed the disease condition within cotton field infected Verticillium wiltcould be reflected by the thematic map of cotton disease. Thus, it is feasible to diagnose the cotton fields with suspected Verticillium wilt by multi-temporal satellite data.

Key words: Cotton, Verticillium wilt, Multi-temporal, TM satellite, Remote-sensing monitoring

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