高光谱分析叶菜对颗粒物污染的响应特征规律

doi:10.3964/j.issn.1000-0593(2021)01-0236-07

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Abstract 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 and λr, 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.

Key words：Particle matters; Hyperspectral; Leaf vegetable; PM2.5; Net photosynthetic rate; Inversion model

Received: 2019-12-25 Accepted: 2020-05-12

ZTFLH:

S129

Corresponding Authors: SUI Yuan-yuan E-mail: suiyuan@jlu.edu.cn

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http://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2021)01-0236-07 OR http://www.gpxygpfx.com/EN/Y2021/V41/I01/236