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Relationship Between Hyperspectral Parameters of Winter Wheat Canopy and Plant Height Components under Late Frost Injury |
SHI Ping1, WU Yong-feng1*, HU Xin2, Lü Guo-hua1, REN De-chao2, SONG Ji-qing1* |
1. Key Laboratory of Agricultural Environment, Ministry of Agriculture, Institute of Environment and Sustainable Development in Agriculture, Chinese Academy of Agricultural Sciences, Beijing 100081, China
2. Shangqiu Academy of Agriculture and Forestry Sciences, Shangqiu 476000, China |
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Abstract After late frost injury, the physiological and ecological aspects of winter wheat were changed, among which the change of plant height was the most significant. In this research, four plant height components, including plant height, ear length, peduncle length, and penultimate internode length, as well as 15 hyperspectral parameters, namely red edge position and the red edge amplitude, etc were obtained. The change rate plant height components, which has the best correlation with the hyperspectral parameters were selected by correlation analysis, and then were used to establish stepwise regression model. The results showed that only the change rate of plant height was significantly correlated with the hyperspectral parameters in both two experiments in 2013 and 2014. After the two experimental data were combined, the change rates of spike length, peduncle length and the penultimate internode length were also significantly correlated. Comprehensively considering the Adj. R2 and the significance level (Sig. ), it can be seen that the best fitting model is the change rate of ear length, followed by the plant height, the peduncle length and the penultimate internode length. Comparing the RMSE of the model, it can be seen that the change rate of peduncle length got the highest prediction precision. The results of this study provide a good reference for the prediction of wheat plant height by hyperspectral parameters under freezing stress conditions. The results are of great significance to the study of the changes of plant height elements in winter wheat under low temperature stress.
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Received: 2016-12-12
Accepted: 2017-04-09
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Corresponding Authors:
WU Yong-feng, SONG Ji-qing
E-mail: wuyongfeng@caas.cn;songjiqing@caas.cn
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[1] Billy E Warrick, Travis D Miller. Texas Agricultural Extension Service, SCS-1999-15, 9.
[2] Rebbeck M, Knell G. In: Reuter D,Ed. Managing Frost Risk: A Guide for Southern Australian Grains. South Australian Research and Development Institute and Grains Research and Development Corporation, Canberra, Australia,2007.
[3] Whaley J M, Kirby E J M, Spink J H, et al. Eur. J. Agron.,2004,21(1):105.
[4] ZHOU Qing-bo, LI Zhang-cheng, LI Sen, et al(周清波,李章成,李 森,等). Acta Agron. Sin.(作物学报),2008, 34(5): 831.
[5] WU Yong-feng,ZHONG Xiu-li,Lü Guo-hua,et al(武永峰,钟秀丽,吕国华,等). Scientia Agricultura Sinica(中国农业科学),2014, 47(21): 4246.
[6] Colombo R, Meroni M, Marchesi A, et al. Remote Sens. Environ.,2008. 112(4):1820.
[7] Zhang J C, Pu R L, Huang W J, et al. Field Crop. Res.,2012,134: 165.
[8] Miller J R, Hare E W, Wu J. International Journal of Remote Sensing, 1998, 11(10): 1755.
[9] M A, Skidmore A K. Remote Sensing of Environment, 2006, 101(2): 181.
[10] WANG Xiu-zhen, HUANG Jing-feng(王秀珍, 黄敬峰). Xinjiang Meteorol(新疆气象), 1996, 19(3): 29. |
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