小麦条锈病胁迫下光合速率与光谱指数关系研究

doi:10.3964/j.issn.1000-0593(2022)03-0940-07

Abstract
References
Related Citation (15)

Download: PDF (2800 KB)
Export: BibTeX | EndNote (RIS)

Abstract For real-time monitor of wheat stripe rust and large-scale recognization of crop diseases using remote sensing technology, the relations of wheat spectral reflectivity and net photosynthetic rate with disease index were studied under stripe rust stress, and the variation of photosynthetic rate was estimated with spectral vegetation indexes. The stripe rust inoculation test was conducted in field plots during the 2018—2019 wheat growth period. The varieties of Jimai 22 and Luyuan 502 with larger sowing areas were used as test materials, and Jimai 15, sensitive to stripe rust, was used as control. The photosynthetic rate and spectral reflectivity of wheat flag leaves were determined, and the disease index was investigated every 7~10 days from heading stage to milk-ripe stage. It was found that the photosynthetic rate decreased significantly with the increase of disease degree. During the flowering stage, the photosynthetic rate of Jimai 22 was higher than that of Luyuan 502. During the grain filling stage, the reflectivity in the visible spectrum range was higher at the diseased part because of lower chlorophyll content leading to lower absorption but the higher reflex of light. However, in the range of reflection platforms, the spectral reflectivity of the diseased part was much lower than that of the healthy part. The indexes related to disease stress, crop growth and yields, such as photochemical reflectance index (PRI), plant senescence reflectance index (PSRI) and ratio vegetation index (RVI) were used to reflect the variation of the disease index. Compared with the healty part, the PRI and PSRI of the diseased part were high, and the change ratio of PSRI was higher; the RVI of diseased part was lower. At different growth stages of wheat, there were different correlations between photosynthetic rate and spectral reflectivity, and the vegetation index was also different. At the grain filling stage, the correlation between photosynthetic rate and spectral reflectivity of Luyuan 502 was positive in all spectrum ranges, and that of Jimai 15 was also positive in visible spectrum range, while that of Jimai 22 was negative. However, in the range of reflectance platform, that of Jimai 15 and Jimai 22 was opposite. The PSRI could be used to recognize disease degree and estimate the photosynthetic rate in the grain filling period of wheat. These results could provide theoretical bases for monitoring wheat growth status and disease occurrence at a large scale using remote sensing method and layed foundations for estimating wheat stripe rust occurrence and degrees using lossless monitoring spectral indicators.

Key words：Wheat; Stripe rust; Photosynthetic rate; Imaging hyperspectral; Estimating model

Received: 2021-02-19 Accepted: 2021-03-25

ZTFLH:

S512.1+1

Corresponding Authors: WANG Li-li, LIU Feng E-mail: 147924249@qq.com；lf.00@163.com

Cite this article:

ZHANG Xiao-yan,HOU Xue-hui,WANG Meng, et al. Study on Relationship Between Photosynthetic Rate and Hyperspectral Indexes of Wheat Under Stripe Rust Stress[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(03): 940-946.

URL:

https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2022)03-0940-07 OR https://www.gpxygpfx.com/EN/Y2022/V42/I03/940

[1] WEN Cai-hong，LI Mei（温彩虹，李酶）. Shaanxi Journal of Agricultural Sciences(陕西农业科技)，2020，66 （11）：56.
[2] DONG Jin-hui，YANG Xiao-dong，YANG Gui-jun，et al（董锦绘，杨小冬，杨贵军，等）. Journal of Triticeae Crops（麦类作物学报），2016, 36(1)：1674.
[3] ZHANG Jing-cheng，YUAN Lin，WANG Ji-hua，et al（张竞成，袁琳，王纪华，等）. Transactions of the Chinese Society of Agricultural Engineering（农业工程学报），2012，28（20）：1.
[4] Zhang M H，Qin Z H，Liu X，et al. Int. J. Appl. Earth Observ. Geoinf.，2003，4：295.
[5] HUANG Mu-yi，WANG Ji-hua，HUANG Wen-jiang，et al（黄木易，王纪华，黄文江，等）. Transactions of the Chinese Society of Agricultural Engineering（农业工程学报），2003，19（6）：154.
[6] YANG Xiao-bing，ZENG Shi-mai（杨小冰，曾士迈）. Science in China (B)（中国科学（B辑）），1988，11：1175.
[7] FENG Wei，WANG Xiao-yu，SONG Xiao，et al（冯伟，王晓宇，宋晓，等）. Acta Agronomica Sinica（作物学报），2013，39（8）：1469.
[8] LÜ Wei，LI Yu-huan，MAO Wei-bing，et al（吕玮，李玉环，毛伟兵，等）. Journal of Agricultural Resources and Environment（农业资源与环境学报），2017，34（6）：582.
[9] Ministry of Agriculture of the People’s Republic of China(中华人民共和国农业部). NY/T 2953—2016 Technical Regulations for Resistance Evaluation of Wheat for Trials to Stripe Rust Caused by Puccinia Striiformis West（NY/T 2953—2016 小麦区域试验品种抗条锈病鉴定技术规程）. 2017.
[10] Rouse J W，Haas R H，Schell J A，et al. Monitoring the Vernal Advancements and Retrogradation of Natural Vegetation. NASA/GSFC，Type Ⅲ，Final Report. MD，USA’ Greenbelt. 1974. 1.
[11] Das D K，Pradhan S，Sehgal V K，et al. Journal of Agrometeorology，2013，15(1): 36.
[12] DENG Shu-bin, CHEN Qiu-jin（邓书斌，陈秋锦）. China Remote Sensing Applications Association 2010 Annual Conference and Regional Remote Sensing Development and Industry High-Level BBS Anthology(中国遥感应用协会2010年年会暨区域遥感发展与产业高层次论坛文集)，2010. 9.
[13] LI He-sheng（李合生）. Modern Plant Physiology（现代植物生理学）. Beijing: Higher Education Press（北京：高等教育出版社）, 2002. 129.
[14] ZHANG Xiao-yan，NIU Lu-yan，SUN Jia-bo，et al（张晓艳，牛鲁燕，孙家波，等）. International Journal of Agriculture and Biology, 2018，(20): 1853.
[15] HE Yong，PENG Ji-yu，LIU Fei，et al（何勇，彭继宇，刘飞，等）. Transactions of the Chinese Society of Agricultural Engineering（农业工程学报），2015，31(3)：174.
[16] ZHANG Qiu-ying，LI Fa-dong，LIU Meng-yu（张秋英，李发东，刘孟雨）. Chinese Journal of Eco-Agriculture（中国生态农业学报），2005，13(3)：95.
[17] YAO Zhi-feng，LEI Yu（姚志凤，雷雨）. Spectroscopy and Spectral Analysis（光谱学与光谱分析），2019, 39（3）：967.