高光谱分析叶菜对颗粒物污染的响应特征规律

大气中的颗粒物不仅影响人类生活，还影响植物的光合作用、生长发育和产量品质。实现了颗粒物污染环境的人工模拟，并对采收期的小白菜、生菜、小油菜三种叶菜进行颗粒物作用试验，获取叶片的光合生理信息和高光谱数据，基于高光谱技术和植物表型分析叶菜对颗粒物的响应机理，研究叶菜的光合特性和光谱特征对颗粒物污染的响应情况。结果表明：以颗粒物作为唯一差别条件下，三种叶菜叶片的高光谱曲线整体趋势相同，在可见光波段内试验组反射率增加最大，红边位置发生蓝移，小油菜对颗粒物的作用最敏感，小白菜吸附颗粒物的能力最强。分别比较三种叶菜的净光合速率与叶片原始光谱、一阶导数光谱的相关性，利用相关分析法提取三种叶菜的敏感波段，用原始光谱、FD、MSC和相关分析法提取特征波长；比较10个高光谱特征参数及4个植被指数与净光合速率的相关系数，选出敏感光谱特征参数和植被指数，即生菜的Dr，SDr，SDr/SDb和SDr/Sdy，小白菜的SDr，Dy，NIRRP，(SDr-SDy)/(SDr+SDy)以及小油菜的λr，SDy，(SDr-SDy)/(SDr+SDy)。用ln对数运算、多项式函数以及几种组合方法建立三种叶菜叶片的净光合速率定量反演模型，其中，预处理方法采用SG，FD，SD和MSC，建模方法采用CLS，PLS，PCR和SMLR。以相关系数为模型评价指标，最终确定FD+SG+PLS方法是建立生菜和小白菜净光合速率反演模型的最优方法，FD+SG+MSC+SMLR方法是建立小油菜净光合速率反演模型的最优方法。所建模型可为今后颗粒物污染环境下的模型修正提供参考，具有实用性。研究结果为利用高光谱技术研究叶菜类蔬菜在颗粒物污染环境下的诊断与分析提供理论依据，为设施农业蔬菜的病害预警、生理信息监测、设施环境的净化和管控提供新思路。

Analyze on the Response Characteristics of Leaf Vegetables to Particle Matters Based on Hyperspectral

Particle matter(PM)pollution not only affects human life but also influences the photosynthesis,growth,yield as well as quality of the plant.In this paper,Pakchoi,oilseed rape(Brassica napus)and Italian lettuce which were at harvest periods were investigated in the simulated particulate pollution environment.The hyperspectral and photosynthetic data of leaf were obtained and their response mechanism to particle matters was studied by hyperspectral technique and plant phenotypic.The spectral and photosynthetic characteristics of leaf vegetables to particle matters were analyzed.The results showed that under the influence of particle matter only,the changing trend of hyperspectral response curves did not alter with the kinds of leaf vegetables,while reflectance value was very different.The leaf showed higher reflectivity within the visible region.The red edge position was"blue shift".Oilseed rape was the most sensitive to PM,and Pakchoi had the strongest ability to absorb PM.The correlations between the net photosynthetic rate and the original spectra,the first derivative spectra,the ten hyperspectral characteristic parameters and the four vegetation indices were compared.The sensitive bands were extracted by the correlation analysis method.The characteristic wavelengths were extracted by original spectrum,first derivative(FD),multiple scatter correction(MSC)and correlation analysis method,and the net photosynthetic rate quantitative inversion model was established by ln logarithm operation,polynomial function and several combination methods.Characteristic parameters were got such as Dr,SDr,SDr/SDb,SDr/Sdy for Italian lettuce,SDr,Dy,NIRRP,(SDr-SDy)/(SDr+SDy)for oilseed rape andλr,SDy,(SDr-SDy)/(SDr+SDy)for Pakchoi.Moverover,the pretreatment methods were FD,second derivative(SD),Savitzky-Golay smooth(SG)and MSC.Also,four modeling methods were classical least squares(CLS),principal component regression(PCR),stepwise multiple linear regression(SMLR)and partial least squares(PLS).The inversion model of the net photosynthetic rate of three kinds of leaves during the collecting period was established with correlated coefficients as an evaluation index of model.The combination of FD+SG+PLS was finally determined the best method for the net photosynthetic rate of Pakchoi and Italian lettuce inversion model,and the combination of FD+SG+MSC+SMLR was finally determined the best method for the net photosynthetic rate of Brassica napus inversion model.The results could provide references for the model modification of leaf vegetables under particle matter pollution environment.This study may provide the theoretical basis for the diagnosis and analysis of physiological changes of leaf vegetables under particle matter pollution using hyperspectral technology,and bring novel ideas for disease identification and early warning of facility agricultural vegetables.