基于PROSPECT模型的蔬菜叶片叶绿素含量和SPAD值反演

doi:10.3964/j.issn.1000-0593(2019)10-3256-05

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Abstract Chlorophyll content is an important indicator for evaluation of plant nutrition and the occurrence of the pests. The traditional spectrophotometry causes damage to plant leaves and can not be used to obtain the chlorophyll content in a real time, fast and non-destructive way. The chlorophyll meter is recently developed to measure the relative content of chlorophyll (referred to as SPAD value). But this method cannot be used to quantitatively obtain the actual content. The optical radiation transmission model PROSPECT can quantitatively describe the effects of leaf pigment, moisture and structural parameters on the reflection spectrum of the leaves with careful consideration of the biophysics, chemistry and the process of energy transfer in the leaves. In the paper, therefore, this model is used to simultaneously inverse the chlorophyll content and SPAD value of vegetable leaves, and obtain the chlorophyll content of plant leaves real-timely, quickly, non-destructively and quantitatively. First, the reflection spectra of the leaves for three vegetables were measured several times, and the SPAD values of these leaves were measured with a chlorophyll meter. Then, the spectral data were preprocessed to obtain the average reflectance spectrum. Second, the averaged reflectance spectra were fitted by the PROSPECT model with the Euclidean distance as the evaluation function. The maximum distance of the three vegetables in the fitting process was 0.008 9, the minimum was 0.006 4, and the average was 0.007 5. Such a low Euclidean distance indicated that the model could well fit the reflectance spectrum of vegetable leaves. Thirdly, according to the fitting results, the chlorophyll content and the transmittance spectrum were inversed, and the inversed SPAD value of the leaves was calculated with the light transmittance of the leaves at 940 and 650 nm as input parameters. Fourth, this paper established a relationship model between the inversed chlorophyll content, the inversed SPAD value and the measured SPAD value. Two main results were obtained: (1) the chlorophyll content obtained by the model has a good linear relationship with the measured SPAD value and the relationship model is y=1.463 3x+16.374 3. The correlation coefficient between them is 0.927 1. The coefficient of determination of the model is 0.862 and the root mean square error is 2.11. (2) A good linear relationship of y=0.986 9x-0.668 3 is obtained for the inversed SPAD value and the measured SPAD value. The correlation coefficient between them is 0.845 1.The coefficient of determination of the model is 0.714 3 and the root mean square error is 3.380 2. The research shows that the PROSPECT model can be used to obtain the chlorophyll content and SPAD value of vegetable leaves nondestructively and quantitatively with the measured reflectance spectrum of plant leaves as input parameters. This method can be extended to other plants for chlorophyll measurement and real-time monitoring and can provide reliable data support for variable rate fertilization and precision planting. The results presented in this paper can be applied to monitor the growth of the vegetables nondestructively.

Key words：Hyperspectral; Vegetable leaf; Chlorophyll content; SPAD value; Inversion

Received: 2018-08-16 Accepted: 2018-12-15

ZTFLH:

O657.3

Corresponding Authors: LONG Yong-bing E-mail: yongbinglong@126.com

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https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2019)10-3256-05 OR https://www.gpxygpfx.com/EN/Y2019/V39/I10/3256

[1] Wang Heng, Qian Xiangjie, Zhang Lan, et al. Frontiers in Plant Science, 2018, 9.
[2] Dimitre A Ivanov, Mark A Bernards. Planta, 2016, 243(1): 263.
[3] CUI Jian-sheng, LÜ Peng-yi(崔建升, 吕鹏翼). Physical Testing and Chemical Analysis Part B: Chemical Analysis(理化检验·化学分册), 2015, 51(1): 105.
[4] Hu Jing, Li Chenxiao, Wen Yifang, et al. IOP Conference Series: Earth and Environmental Science, 2018, 108(2).
[5] DING Yong-jun, LI Min-zan, AN Deng-kui, et al（丁永军, 李民赞, 安登奎, 等）. Transactions of the Chinese Society of Agricultural Engineering（农业工程学报）, 2011, 27(5): 244.
[6] Li Dong, Cheng Tao, Kai Zhou, etal. ISPRS Journal of Photogrammetry and Remote Sensing, 2017, 129.
[7] Lu Xia, Peng Hongchun. Journal of the Indian Society of Remote Sensing, 2015, 43(1): 109.
[8] Frédéric Baret, Vern C Vanderbilt, Michael D Steven, et al. Remote Sensing of Environment, 1994, 48: 253.
[9] Jiang Jingyi, Comar Alexis, Burger Philippe, et al. Plant Methods, 2018, 14.
[10] Sun Jia, Shi Shuo, Yang Jian, etal. Remote Sensing of Environment, 2018, 212.
[11] SUN Jun, JING Xia-ming, MAO Han-ping, et al（孙俊, 金夏明, 毛罕平，等）. Transactions of the Chinese Society of Agricultural Engineering（农业工程报）, 2014, 30(10): 167.
[12] Féret J-B, Gitelson A A, Noble S D, etal. Remote Sensing of Environment, 2017, 193.
[13] Gu Chengyan, Du Huaqiang, Mao Fangjie, etal. International Journal of Remote Sensing, 2016, 37(22): 5270.
[14] Asim Banskota, Shawn P Serbin, Randolph H Wynne, et al. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, 8(6): 3147.
[15] SU Hong-sheng, YIN Kai-le（苏宏升, 殷凯乐）. Computer Engineering and Applications（计算机工程与应用）, 2016, 52(24): 50.
[16] YANG Ren-jie, YANG Yan-rong, DONG Gui-mei, et al（杨仁杰, 杨延荣, 董桂梅, 等）. Spectroscopy and Spectral Analysis（光谱学与光谱分析）, 2014, 34(8): 2098.
[17] Casa, R, et al. The Journal of Agricultural Science，2015, 153(5): 876.
[18] Angela De Santis, Emilio Chuvieco, Patrick J Vaughan. Remote Sensing of Environment, 2009, 113(1): 126.
[19] GU Cheng-yan, DU Hua-qiang, ZHOU Guo-mo，et al（谷成燕, 杜华强, 周国模, 等）. Chinese Journal of Applied Ecology, 2013, 24(8): 2248.