The Study Of Winter Wheat Growth Monitoring By Using Imaging Spectrometer Near Ground

【Objective】In order to elucidate spectrum characteristics of wheat canopy, Imaging spectrometer is a new kind of remote sensing sensor, it receives images of ground objects as well as their spectrum components. Due to carrying high spatial and spectral resolutions, imaging spectrometer has great potentials in accurately quantitative analysis with remote sensing at large scale using airborne and airspace remote sensing as other crop.【Method】Used datas that were collected both wheat canopy and single leaf spectrum and in different treatments in the year 2006～2008 at the experiment station of Chinese Academy of Agricultural Sciences located in Beijing, meanwhile from canopy and single leaf were investigated. Through qualitatively and quantitatively analyzed the correlation between reflectance spectrum,derivative spectra of wheat canopy and agricultural parameters, we set up spectrum identify and inversion model of CCD,COV,COBI on wheat canopy.【Result】The results indicated that DN values of wheat canopy from MSI200 and ASD Fieldspc FR2500 are different while the spectrum curves are identical,their reflectivity and spectral parameters are significant at P<0.01.The imaging spectrometer MSI200 can demonstrate object through image and extract the pure spectral information that can be use to distinguish and engender single image.By using this method can restrain shadow,soil and other yawps evidently when identifying wheat leaves. Making use of spectral parameters from MSI200 to monitor winter wheat canopy Chlorophyll density,coverage density and biomass , the results showed that PPR can predict CCD, GNDVI can predict COV, TCARI can predict COBI, R550 can predict LNC ,and the models have higher precision and lower error.The RMSE of Models based on MSI200 data are 0.0979,0.0107,0.6197 and 0.2148,and their RE are 4.46%,8.53%,7.92% and 5.46%.These models have higher precision than based on ASD Fieldspc FR2500,and can well estimate CCD, COV, COBI and LNC of wheat.【Conclusion】The results showed that data from MSI 200 were reliable and using MSI 200 can well predict wheat canopy chlorophyll density , conopy coverage density and conopy biomass than traditional non- imaging spectrometer.There are the best recognizing models: CCD = 0.4286 PPR - 0.0818 R~2=0.7949 n=20 COV = 224.98G NDVI - 33.738 R~2 = 0.9016 n=20 COBI= -559.5 TCARI + 98.084 R~2 = 0.7988 n=20 LNC= -0.1301 R550 + 6.596 R~2 = 0.9338 n=20...