日光诱导叶绿素荧光与反射率光谱数据监测小麦条锈病严重度的对比分析

doi:10.3964/j.issn.1000-0593(2019)09-2739-07

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Abstract Stripe rust of wheat is one of the hazardous diseases which affects the wheat yield in China. It is more significant to early detect wheat stripe rust infection information for the prevention of wheat stripe rust and the improvement of yield and quality. Considering that reflectance spectra are sensitive to variations in the concentration of plant biochemical components, and the sun-induced chlorophyll fluorescence is sensitive to variations in plant photosynthetic physiology. In order to preferably detect the severity of wheat stripe rust disease by remote sensing, especially the earlier detection of wheat stripe rust disease, this study made a comparative analysis of the sensitivity of sun-induced chlorophyll fluorescence and reflectance spectrum data to monitor the severity of wheat stripe rust disease. First used the ASD Field Spec Pro NIR spectrometer to determine the wheat canopy spectral data of different illness severity, on the basis of the principle of fraunhofer line to extracted sun-induced chlorophyll fluorescence data by the method of 3FLD under different illness severity, then respectively induced by reflectance spectra data and sun-induced chlorophyll fluorescence data to construct at different conditions of wheat stripe rust of remote sensing detection model, and through the retained sample cross terms of inspection on the forecast model accuracy is evaluated. The result shows that: (1) when the severity of wheat stripe rust disease was less than 20%, the sun-induced chlorophyll fluorescence response of wheat stripe rust disease information was more sensitive than reflectance spectra data, and the sun-induced chlorophyll fluorescence as the independent variable to build the forecasting model of wheat stripe rust disease severity reached the extremely significant level. It can earlier diagnose the crop diseases by detecting the stress state of plants before the change of chlorophyll content or leaf area index, while it is hard to use the reflectivity spectrum data to detect wheat stripe rust damage information. (2) when the severity of wheat stripe rust disease is in the state of moderate incidence (20%45%), the prediction model of severity of wheat stripe rust disease constructed by using reflectance spectral data and sun-induced chlorophyll fluorescence data has reached the extremely significant level, both of which can preferably detect the severity of wheat stripe rust by remote sensing. The results of this study have great significance for improving the remote sensing detection accuracy of wheat stripe rust, and it provides reference basis for the earlier detection of stripe rust in wheat by using TanSat or other satellite fluorescence data.

Key words：Wheat stripe rust; Three-band fraunhofer line discrimination; Sun-induced chlorophyll fluorescence; Vegetation index; Differential spectrum; Severity

Received: 2018-12-06 Accepted: 2019-04-18

ZTFLH:

S512.1

Corresponding Authors: JING Xia E-mail: jingxia1001@163.com

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https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2019)09-2739-07 OR https://www.gpxygpfx.com/EN/Y2019/V39/I09/2739

[1] LI Guo-yang, CHEN Jing-mei(李国阳，陈景梅). Application Technology of High Yield and High Quality Wheat Production(小麦高产优质生产应用技术). Beijing: China Agricultural Science and Technology Press(北京：中国农业科学技术出版社), 2015. 11.
[2] Shi Y, Huang W J, Luo J H, et al. Computers and Electronics in Agriculture，2017, 141: 171.
[3] WANG Shuang, MA Zhan-hong, SUN Zhen-yu, et al(王爽, 马占鸿, 孙振宇, 等). Chinese Agricultural Science Bulletin(中国农学通报), 2011，27(21): 253.
[4] DONG Jin-hui, YANG Xiao-dong, YANG Gui-jun, et al(董锦绘，杨小冬，杨贵军，等). Journal of Wheat Crops(麦类作物学报), 2016, 36(12): 1674.
[5] LIU Liang-yun(刘良云). Principle and Application of Quantitative Remote Sensing for Vegetation(植被定量遥感原理与应用). Beijing: Science Press(北京: 科学出版社), 2014.
[6] Raji S N, Subhash N, Ravi V, et al. International Journal of Remote Sensing，2015，36(11)：2880.
[7] Liu L, Zhao J, Guan L. European Journal of Remote Sensing, 2013, 46(1)：459.
[8] LI Cui-ling, JIANG Kai, FENG Qing-chun, et al(李翠玲，姜凯，冯青春，等). Spectroscopy and Spectral Analysis(光谱学与光谱分析)，2018, 38(1)：151.
[9] He W Z, Lin Y, Leng Y F, et al. Journal of Phytopathology，2018，166(3)：167.
[10] Pieczywek P M, Cybulska J, Szymańska-Chargot M, et al. Food Control, 2018，85：327.
[11] Hu H, Li S, Zhang G Z, et al. Journal of Phytopathology, 2018, 166(7-8)：547.
[12] YANG Zhi-xiao, DING Yan-fang, ZHANG Xiao-quan, et al(杨志晓, 丁燕芳, 张小全, 等). Journal of Ecology(生态学报), 2015, 35(12): 4146.
[13] Armando Sterling, Luz Marina Melgarejo. Physiological and Molecular Plant Pathology, 2018，103：143.
[14] Sun Y M, Wang M, Li Y R, et al. Annals of Botany，2017，120(3)：427.
[15] Chiu Yi-Chich, Hsu Wei-Chih, Chang Yung-Chiung, et al. Environment and Food, 2015，8(5)：95.
[16] da Silva André Costa, de Oliveira Silva Franklin Magnum, Milagre Jocimar Caiafa，et al. Plant Physiology and Biochemistry, 2018，123：170.
[17] Bauriegel E, Brabandt H, Gärber U, et al. Computers and Electronics in Agriculture，2014，105：74.
[18] WANG Ran, LIU Zhi-gang, YANG Pei-qi(王冉，刘志刚，杨沛琦). Advances in Earth Science(地球科学进展), 2012, 27(11): 1221.
[19] Maier S W, Günther K P, Stellmes M. American Society of Agronomy, 2003. 209.
[20] Hunt E Raymond，Doraiswamy Paul C，McMurtrey James E，et al. International Journal of Applied Earth Observations and Geoinformation, 2013, 21: 103.