Simulation and estimation of photosynthetic parameter of turfgrass based on hyperspectrum
[Objective]The photosynthetic parameters are important physiological indicators for assessing the growth status of turfgrass.It is of great significance for turf maintenance management to explore the simulated estima-tion of turfgrass photosynthetic parameters based on hyperspectral technology.[Method]In this experiment,three commonly used turfgrass species,"Hongxiang tall"fescue(Festuca arundinacea cv.Hongxiang),"Bailingniao"pe-rennial ryegrass(Lolium perenne cv.Bailingniao),and"Kentucky"Kentucky bluegrass(Poa pratensis cv.Ken-tucky),were selected as experimental materials.During the vigorous growth period of turfgrass,spectral data of turf-grass canopy,net photosynthetic rate(Pn),and transpiration rate(Tr)were measured using the SOC710VP imaging spectrometer and the CIRAS-3 portable photosynthesis system.The original spectral bands and vegetation indices sig-nificantly correlated with the two photosynthetic parameters were selected through pot experiments.Partial least squares(PLS)regression models were constructed,and the Variable Importance Projection(VIP)method was used to screen important spectral bands and vegetation indices with VIP values>1.2 in the PLS model.[Result]1)A to-tal of 54 original spectral bands(435,450,460,475,490～550,560～565,590～725,990～1000 nm,1015～1030 nm)and 9 vegetation indices(GI,NDVI,NDVI670,CI,PSRI,NRI,SIPI,PRI,SR)significantly correlated with Pn were selected.Among them,the absolute values of correlation coefficients between the original spectral band at 460 nm and the vegetation index CI were 0.46 and 0.77,respectively.A total of 115 original spectral bands(435～440 nm,450～1010 nm)and 7 vegetation indices(SIPI,SR,NDVI,NDVI670,MSR705,CI,DVI)significantly corre-lated with Tr were selected.Among them,the original spectral band at 475nm and the vegetation index SIPI had the highest absolute correlation coefficients of 0.61 and 0.54,respectively.2)The PLS regression model for Pn had a variance explanation rate of 75.24%,a model fitting accuracy(R2)of 0.95,and a root mean square error(RMSE)of 0.1,while the PLS regression model for Tr had a variance explanation rate of 73.43%,an R2 of 0.73,and an RMSE of 0.5,meeting the requirements of inversion.3)According to the VIP method in the PLS regression,the optimal in-dicator for estimating Pn was CI,and the optimal indicator for estimating Tr was SR.[Conclusion]The PLS regres-sion spectral inversion models for the net photosynthetic rate and transpiration rate of turfgrass provide a more conve-nient solution for the assessment of turfgrass photosynthetic indicators.
hyperspectrumturfgrassnet photosynthetic ratetranspiration ratepartial least squares model
NETL
NSTL
万方数据
