摘要
The nondestructive and rapid acquisition of rice field phenotyping information is very important for the precision management of the rice growth process.In this research,the phenotyping information LAI(leaf area index),leaf chlorophyll content(C_(ab)),canopy water content(C_(w)),and dry matter content(C_(dm))of rice was inversed based on the hyperspectral remote sensing technology of an unmanned aerial vehicle(UAV).The improved Sobol global sensitivity analysis(GSA)method was used to analyze the input parameters of the PROSAIL model in the spectral band range of 400-1100 nm,which was obtained by hyperspectral remote sensing by the UAV.The results show that C_(ab) mainly affects the spectrum on 400-780 nm band,C_(dm) on 760-1000 nm band,C_(w) on 900-1100 nm band,and LAI on the entire band.The hyperspectral data of the 400-1100 nm band of the rice canopy were acquired by using the M600 UAV remote sensing platform,and the radiance calibration was converted to the canopy emission rate.In combination with the PROSAIL model,the particle swarm optimization algorithm was used to retrieve rice phenotyping information by constructing the cost function.The results showed the following:(1)an accuracy of R^(2)=0.833 and RMSE=0.0969,where RMSE denotes root-mean-square error,was obtained for C_(ab) retrieval;R^(2)=0.816 and RMSE=0.1012 for LAI inversion;R^(2)=0.793 and RMSE=0.1084 for C_(dm);and R^(2)=0.665 and RMSE=0.1325 for C_(w).The C_(w) inversion accuracy was not particularly high.(2)The same band will be affected by multiple parameters at the same time.(3)This study adopted the rice phenotyping information inversion method to expand the rice hyperspectral information acquisition field of a UAV based on the phenotypic information retrieval accuracy using a high level of field spectral radiometric accuracy.The inversion method featured a good mechanism,high universality,and easy implementation,which can provide a reference for nondestructive and rapid inversion of rice biochemical parameters using UAV hyperspectral remote sensing.
基金
support of the National Key Research and Development Plan of China(Grant No.2016YFD020060307)
Key Project of Education Department of Liaoning province(LSNZD201605).