基于高光谱的草坪草光合参数模拟估算  

Simulation and estimation of photosynthetic parameter of turfgrass based on hyperspectrum

作　　者：刘桐 杜笑村 纪童 姜佳昌 LIU Tong;DU Xiao-cun;JI Tong;JIANG Jia-chang(Xi′an Siyuan College,Xi′an 710038,China;Gansu Grassland Technology Promotion General Station,Lanzhou 730010,China;College of Grassland Science,Gansu Agricultural University,Key Laboratory for Grassland Ecosystem,Ministry of Education,Grassland Engineering Laboratory of Gansu Province,Sino-U.S.Centers for Grazing Land Ecosystem Sustainability,Lanzhou 730070,China)

机构地区：[1]西安思源学院,陕西西安710038 [2]甘肃省草原技术推广总站,甘肃兰州730010 [3]甘肃农业大学草业学院,草业生态系统教育部重点实验室,甘肃省草业工程实验室,中-美草地畜牧业可持续发展研究中心,甘肃兰州730070

出　　处：《草原与草坪》2024年第2期79-87,共9页Grassland and Turf

基　　金：林草科技创新与国家合作项目(lckjcx202303)。

摘　　要：【目的】光合参数是衡量草坪草生长状况的重要生理指标,探索基于高光谱技术的草坪草光合参数的模拟估算对于草坪养护管理具有重要意义。【方法】以3个常用草坪草品种红象高羊茅(Fes⁃tuca arundinacea cv.Hongxiang)、百灵鸟多年生黑麦草(Lolium perenne cv.Bailingniao)和肯塔基草地早熟禾(Poa pratensis cv.Kentucky)为试验材料,通过盆栽试验,在草坪草生长旺盛期,使用SOC710VP成像光谱仪和CIRAS⁃3便携式光合仪分别测定了草坪草冠层光谱数据、净光合速率(P_(n))和蒸腾速率(T_(r)),筛选与两种光合参数显著相关的原始光谱波段与植被指数,构建偏最小二乘法(PLS)估算模型,并利用PLS模型中变量投影重要性(Variable Importance Projection,VIP)方法筛选VIP值>1.2的重要波段与植被指数。【结果】1)共筛选与P_(n)显著相关的54个原始光谱波段(435、450、460、475、490~550、560~565、590~725、990~1000、1015~1030 nm)与9个植被指数(GI、NDVI、NDVI_(670)、CI、PSRI、NRI、SIPI、PRI、SR),其中原始光谱460 nm与植被指数CI相关系数绝对值最高,分别为0.46和0.77,共筛选与T_(r)显著相关的115个原始光谱波段(435~440、450~1010 nm)与7个植被指数(SIPI、SR、NDVI、NDVI_(670)、MSR_(705)、CI、DVI),其中原始光谱475 nm与植被指数SIPI相关系数绝对值最高,分别为0.61与0.54;2)P_(n)偏最小二乘法模型因变量方差解释率为75.24%,模型拟合精度R2为0.95,均方根误差RMSE为0.1,T_(r)偏最小二乘法模型因变量方差解释率为73.43%,模型拟合精度R2为0.73,均方根误差RMSE为0.5,可满足反演需求;3)根据偏最小二乘法中变量投影重要性VIP法筛选最优指标,得出反演P_(n)的最优指标为CI,T_(r)最优指标为SR。【结论】草坪草净光合速率与蒸腾速率的偏最小二乘法光谱反演模型,为草坪草光合指标评估提供了更便利的方案。【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”perennial 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(P_(n)),and transpiration rate(T_(r))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,NDVI_(670),CI,PSRI,NRI,SIPI,PRI,SR)significantly correlated with P_(n) 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,NDVI_(670),MSR_(705),CI,DVI)significantly corre⁃lated with T_(r) 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 P_(n) had a variance explanation rate of 75.24%,a model

关 键 词：高光谱 草坪草 净光合速率 蒸腾速率 偏最小二乘法模型 

分 类 号：S688.4[农业科学—观赏园艺]