Study on Estimation of wheat canopy FPAR based on hyper-spectral technology

After analyzing the dynamic development regulation between wheat Canopy Spectra and RPAR at different growth stages, we studies the quantitative relationship between wheat Canopy Spectra and RPAR in order to build the models which can predict the value of wheat FPAR in different areas through the technology of hyper-spectral remote sensing.We selected the Houhu County in Qianjiang City Hubei Province and Changqing County of Jinan City,Shandong Province as the test areas, and collected reflection spectrum and the FPAR value of Zhengmai 9023,Wanmai 369, Linmai 2,Wenqian 1 and Tainong 18 respectively as the database for analysis. By regression analyzing, we find that the correlation coefficientbetween NDVI and FPAR reach 0.889 and create prediction models between 6 vegetation indexes (RVI,DVLNDVI,GRVI,EVI and SAVI) and FPAR. Tests on the accuracy of the 6 prediction models indicate that The NDVI log-additive model have the highest accuracy for predicting FPAR. We use the sample data collected in Houhu County and Changqing County to validate the precision of the model, which shows the NDVI log-additive estimating model is suitable for predicting FPAR of different wheat variety indifferent areas and can provide an accurate technique for monitoring the growth of wheat and estimating the yields.