光谱学与光谱分析, 2014, 34 (5): 1357, 网络出版: 2014-05-06  

冬小麦冻害胁迫高光谱分析与冻害严重度反演

Monitoring Freeze Stress Levels on Winter Wheat from Hyperspectral Reflectance Data Using Principal Component Analysis
作者单位
1 中国气象科学研究院, 北京100081
2 北京农业信息技术研究中心, 北京市农林科学院, 北京100097
摘要
对冬小麦冻害严重度的精确反演是及时采取补救措施降低损失的关键, 同时及时预测产量损失对政府职能部门也具有积极意义。 针对冬小麦冻害群体严重度评估方法在经典统计反演模型存在估算效果不理想的情况下, 以冬小麦为试验对象, 首先对冬小麦冠层光谱反射率数据进行重采样平滑处理, 再用主成分分析(PCA)技术对高光谱数据进行分析, 进一步实现综合原始光谱主成分信息作为自变量参与冬小麦冻害严重度反演过程, 最后采用决定系数R2、 均方根误差RMSE、 准确度Accuracy三种模型精度验证方法对模型进行评价。 结果显示, 基于主成分分析法建立冬小麦冻害严重度模型精度分别达0.697 5, 0.184 2和0.697 5; 同时对反演模型进行验证, 其精度也分别达到0.630 9, 0.350 3和1.339 6。 因此, 该方法能有效地对冬小麦冻害严重度进行快速、 精确的反演。
Abstract
In order to detect the freeze injury stress level of winter wheat growing in natural environment fast and accurately, the present paper takes winter wheat as experimental object. First winter wheat canopy hyperspectral data were treated with resampling smooth. Second hyperspectral data were analyzed based on principal components analysis (PCA), a freeze injury inversion model was established, stems survival rate was dependent, and principal components of spectral data were chosen as independent variables. Third, the precision of the model was testified. The result showed that the freeze injury inversion model based on 6 principal components can estimate the winter wheat freeze injury accurately with the coefficient of determination (R2) of 0.697 5, root mean square error (RMSE) of 0.184 2, and the accuracy of 0.697 5. And the model was verified. It can be concluded that the PCA technology has been shown to be very promising in detecting winter wheat freeze injury effectively, and provide important reference for detecting other stress on crop.
参考文献

[1] WANG Jian-lin, LIN Ri-nuan(王健林, 林日暖). Agricultural Meteorological Disasters in Western China(中国西部农业气象灾害). Beijing: Beijing Meteorology Impress(北京气象出版社), 2003. 1.

[2] DONG Yan-sheng, CHEN Hong-ping, WANG Hui-fang, et al(董燕生, 陈洪萍, 王慧芳, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2012, 28(20): 172.

[3] Li Jing, Jiang Jinbao, Chen Yunhao, et al. New Zealand Journal of Agricultural Research, 2007, 50: 1031.

[4] Wu Chaoyang, Niu Zheng, Tang Quan, et al. Agricultural and Forest Meteorology, 2008, 148: 1230.

[5] Liu Meiling, Liu Xiangnan, Ding Weicui, et al. International Journal of Applied Earth Observation and Geoinformation, 2011, 13: 246.

[6] ZHANG Rui, MA Jian-wen(张睿, 马建文). Geomatics and Information Science of Wuhan University(武汉大学学报·信息科学版), 2009, 34(7): 834.

[7] YANG Xiao-hua, WU Yao-ping, HUANG Jing-feng(杨晓华, 吴耀平, 黄敬峰). China Series C-Life Sciences(中国科学C辑: 生命科学) 2009, 39(11): 1080.

[8] Hansen P M, Schjoerring J K. Remote Sensing of Environment, 2003, 86: 542.

[9] Martin M E, Plourde L C, Ollinger S V, et al. Remote Sensing of Environment, 2008, 112: 3511.

[10] LU Yan-li, BAI You-lu, YANG Li-ping, et al(卢艳丽, 白由路, 杨俐苹, 等). Plant Nutrition and Fertilizer Science(植物营养与肥料学报), 2008, 4(6): 1076.

[11] Neil Lawrence. The Journal of Machine Learning Research, 2005, 6: 1783.

[12] Zhang M, Liu X, O’Neill M. International Journal of Remote Sensing, 2002, 53(6): 1095.

[13] Cheng X, Chen Y R, Tao Y, et al. Transactions of the ASAE, 2004, 47(4): 1313.

[14] Liu Y, Chen Y R, Wang C Y, et al. Applied Engineering in Agriculture, 2006, 22(1): 101.

[15] ElMasry G, Wang N, ElSayed, et al. Journal of Food Engineering, 2007, 81(1): 98.

[16] DUANG Zhong-hong, DONG Fang-hong, ZHANG Lu-xian(段忠红, 董方红, 张路线). Agricultural Technology & Equipment(农业技术与装备), 2010, 198: 21.

王慧芳, 王纪华, 董莹莹, 顾晓鹤, 霍治国. 冬小麦冻害胁迫高光谱分析与冻害严重度反演[J]. 光谱学与光谱分析, 2014, 34(5): 1357. WANG Hui-fang, WANG Ji-hua, DONG Ying-ying, GU Xiao-he, HUO Zhi-guo. Monitoring Freeze Stress Levels on Winter Wheat from Hyperspectral Reflectance Data Using Principal Component Analysis[J]. Spectroscopy and Spectral Analysis, 2014, 34(5): 1357.

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