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《Jiangsu Journal of Agricultural Sciences》 2009-06
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Classified Monitoring of Grain Protein Contents of Winter Wheat by TM Image Based on ISODATA

LI Wei-guo~1,LI Zheng-jin~(1,2),WANG Ji-hua~3,HUANG Wen-jiang~3,GUO Wen-shan~4 (1.Institute of Agricultural Resources and Environment,Jiangsu Academy of Agricultural Sciences,Nanjing 210014,China;2.College of Applied Meteorology, Nanjing University of Information Science & Technology,Nanjing 210044,China;3.National Engineering Research Center for Information Technology in Agriculture,Beijing 100089,China;4.College of Agriculture,Yangzhou University,Yangzhou 225009,China)  
Estimating the grain protein contents in winter wheat in different growth stages by remote sensing technology may help farmers to acquire the growth information of winter wheat timely so as to achieve good quality.The protein contents in winter wheat was monitored using the remote sensing technology based on land-sat TM image in Jiangyan city, Jiangsu Province.By November 2008,20 test sites were distributed almost all over the county.The geographical position and some other information of these samples such as areas' shapes had been measured by the handhold GPS machines.The GPS data and the interpretation mark were used to correct TM image,verify the unsupervised classification,assist human- computer interactive interpretation,and other operations. The test data participated the whole classification process based on ISODATA.The leaf area index(LAI) and the plant nitrogen accumulation got from the normalized difference vegetation index(NDVI) inversion and the biomass from the ratio regetation index(RVI) inversion at anthesis, combined with the protein content estimation model of winter wheat grain were applied to the winter wheat grain protein content monitoring,and made a winter wheat quality production grading thematic map.The accuracy of interpreted information for winter wheat area was more than 95%,and the estimation accuracy of the grain protein contents was above 90%.
【Fund】: 国家“863”计划项目(2008AA10Z214);; 农业部行业科技项目(200803037);; 江苏省农业科学院人才基金项目(6510805);; 江苏省农业科学院科研基金项目(6110824)
【CateGory Index】: S512.1
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