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The Study Of Hyperspectral Remote Sensing Models Of Corn Major Biophycial And Biochemical Parameters

Posted on:2006-08-30Degree:MasterType:Thesis
Country:ChinaCandidate:Q X YiFull Text:PDF
GTID:2133360155950894Subject:Environmental Science
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High spectral resolution (Hyperspectral) remote sensing, which is based on spectroscopy, refers to use many narrow electromagnetic wavebands to detect the spectral reflectance of our interesting objects. Different from multispctral remote sensing, hyperspectral remote sensing shows many advantages, such as high spectral resolution, good continuity of wavebands and considerable contents of spectral information. In this study, we select corn as our interesting object, and use ASD FieldSpec Pro FR? to obtain the spectral reflectance of corn. We carried field-spectral observations at corn different growth period and obtained spectral reflectance of corn canopy as well as of corn different organs, which include leaves, stems, leaf veins and flowers and so on. Besides, the corresponding agricultural parameters were measured at the same time. The major biophysical parameters include the above ground fresh biomass, the above ground dry biomass, leaf area index. The major biochemical parameters include nitrogen concentration, rough fattiness concentration, rough fiber concentration and pigment concentration (chlorophyll concentration, chlorophyll a concentration, chlorophyll b concentration and carotenoid concentration). Excel software and SPSS (Statistical Package for Social)are used to process data. Multifactor linear stepwise regression technique and curve fitness analysis are adopted to discuss the correlation between corn agronomic parameters and raw hyperspectral reflectance as well as its different transformations, which include its first derivation spectral reflectance, hyperspectral characteristic variables, vegetation indices. And then the hyperspectral remote sensing estimation models for different agronomic parameters are constructed based on the results of correlation analysis, finally, the predictive precisions are analyzed for some chosen models in order to determine the best estimation models for every agronomic parameters. The results are following: The above ground fresh biomass : FB=0.001exp17.16(SDr-SDb)/(SDr+SDb) (n=30,R2=0.914) The above ground dry biomass: DB=0.0002exp16.912(SDr-SDb)/(SDr+SDb) (n=30,R2=0.893) LAI:LAI=669.573 Dr -0.962 (n=30,R2=0.831) Nitrogen concentration: Nitrogen =1.369exp1457.1ρ759 (n=35,R2=0.891) (Note:ρ759 refers to the first derivative spectral reflectance at 759nm) Rough fiber concentration: Fibre=-422.53ρ393 +42.42 (n=35,R2=0.513) (Note:ρ393 refers to the raw spectral reflectance at 393nm)Rough fattiness concentration: Fattiness=0.115Λr +11.888 Ro -79.498 (n=35,R2=0.761) Chlorophyll concentration: Chlorophyll=5.91exp-28.962 SDb (n=90,R2=0.817) Chlorophyll a concentration: Chla=4.763exp-29.803 SDb (n=90,R2=0.818) Chlorophyll b concentration: Chlb=1.198exp-26.504 SDb (n=90,R2=0.811) Carotenoid concentration: Carotenoid=1.845exp-817.93 Db (n=90,R2=0.754)...
Keywords/Search Tags:hyperspctral, corn, agronomic parameters, hyperspectral variables
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